diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index be24660..c7a87fa 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -21,12 +21,8 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install pipenv
-        pipenv install --dev
-    - name: Lint with flake8
-      run: |
-        pip install flake8
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        python setup.py develop
+
         
     - name: Test with unittest
       run: |
diff --git a/.gitignore b/.gitignore
index 587f4ae..9bc5e90 100644
--- a/.gitignore
+++ b/.gitignore
@@ -49,6 +49,12 @@ coverage.xml
 .hypothesis/
 .pytest_cache/
 
+# Models
+*.model
+*.npy
+*.npz
+*.pkl
+
 # Translations
 *.mo
 *.pot
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000..b3abbec
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1 @@
+include txt2hpo/data/*.wv.gz
\ No newline at end of file
diff --git a/Pipfile b/Pipfile
index f73b407..498d3e5 100644
--- a/Pipfile
+++ b/Pipfile
@@ -11,8 +11,11 @@ pytest-cov = "*"
 [packages]
 nltk = "*"
 spacy = "*"
+scispacy = "*"
+negspacy = "*"
 phenopy = "*"
 networkx = "*"
+gensim = "*"
 
 [requires]
 python_version = "3.7"
diff --git a/Pipfile.lock b/Pipfile.lock
index 666c40c..2c5545f 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "eefedff5ab3352737afd2b35e569f63fabc9802dd317665f8ba471c3be5c1201"
+            "sha256": "8761be25382a2935c8ad0b3066dfccbf4c9b9e57ac798152fdfee7e3905ab676"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -16,8 +16,16 @@
         ]
     },
     "default": {
+        "awscli": {
+            "hashes": [
+                "sha256:7bf8e31f83e79c9c5ab07fc7b82fdc15765f9c89140d71bda63e63f218ced859",
+                "sha256:ef62f799c4c72614933e3ba28a5aaefe88deed8d1886ca173f452da36add445a"
+            ],
+            "version": "==1.18.1"
+        },
         "blis": {
             "hashes": [
+                "sha256:00473602629ba69fe6565108e21957e918cb48b59f5bf2f6bfb6e04de42500cb",
                 "sha256:03c368c9716ca814c436550a5f1e02ccf74850e613602519e3941d212e5aa177",
                 "sha256:135450caabc8aea9bb9250329ebdf7189982d9b57d5c92789b2ba2fe52c247a7",
                 "sha256:1402d9cbb0fbc21b749dd5b87d7ee14249e74a0ca38be6ecc56b3b356fca2f21",
@@ -26,7 +34,9 @@
                 "sha256:38fe877a4b52e762f5e137a412e3c256545a696a12ae8c40d67b8815d2bb5097",
                 "sha256:4fb89c47ee06b58a4410a16fd5794847517262c9d2a342643475b477dfeff0a4",
                 "sha256:77a6486b9794af01bcdfd1bc6e067c93add4b93292e6f95bf6e5ce7f98bf0163",
+                "sha256:856142a11e37fd2c47c5006a3197e157bb8469a491a73d2d442223dd3279df84",
                 "sha256:8aeaf6954351593a1e412f80e398aa51df588d3c0de74b9f3323b694c603381b",
+                "sha256:9ede123065f3cacb109967755b3d83d4ca0de90643a9058129a6ab2d4051954f",
                 "sha256:d1d59faebc1c94f8f4f77154ef4b9d6d40364b111cf8fde48ee3b524c85f1075",
                 "sha256:d69257d317e86f34a7f230a2fd1f021fd2a1b944137f40d8cdbb23bd334cd0c4",
                 "sha256:ddd732c5274d1082fa92e2c42317587d5ebabce7741ca98120f69bd45d004b99",
@@ -34,12 +44,40 @@
             ],
             "version": "==0.4.1"
         },
+        "boto": {
+            "hashes": [
+                "sha256:147758d41ae7240dc989f0039f27da8ca0d53734be0eb869ef16e3adcfa462e8",
+                "sha256:ea0d3b40a2d852767be77ca343b58a9e3a4b00d9db440efb8da74b4e58025e5a"
+            ],
+            "version": "==2.49.0"
+        },
+        "boto3": {
+            "hashes": [
+                "sha256:666bbcc7616b79e35d4ecb5fea3624d7812ec9d0bff13919501942af9e836d23",
+                "sha256:f1d5be915b806bd4335757040fb9741587b4b72bb85b9ee2fe91fe1d1655aa69"
+            ],
+            "version": "==1.12.1"
+        },
+        "botocore": {
+            "hashes": [
+                "sha256:5cd1076e953eac0d2da3d499d8b6641e85fe62abe4ad9e40298b6826a3e95bcb",
+                "sha256:7f533d7ffcf4c3655bb2b37d6dbd2b067ba06074b9f186d66e8d6fcb90812987"
+            ],
+            "version": "==1.15.1"
+        },
+        "catalogue": {
+            "hashes": [
+                "sha256:584d78e7f4c3c6e2fd498eb56dfc8ef1f4ff738480237de2ccd26cbe2cf47172",
+                "sha256:d74d1d856c6b36a37bf14aa6dbbc27d0582667b7ab979a6108e61a575e8723f5"
+            ],
+            "version": "==1.0.0"
+        },
         "certifi": {
             "hashes": [
-                "sha256:e4f3620cfea4f83eedc95b24abd9cd56f3c4b146dd0177e83a21b4eb49e21e50",
-                "sha256:fd7c7c74727ddcf00e9acd26bba8da604ffec95bf1c2144e67aff7a8b50e6cef"
+                "sha256:017c25db2a153ce562900032d5bc68e9f191e44e9a0f762f373977de9df1fbb3",
+                "sha256:25b64c7da4cd7479594d035c08c2d809eb4aab3a26e5a990ea98cc450c320f1f"
             ],
-            "version": "==2019.9.11"
+            "version": "==2019.11.28"
         },
         "chardet": {
             "hashes": [
@@ -48,33 +86,37 @@
             ],
             "version": "==3.0.4"
         },
+        "colorama": {
+            "hashes": [
+                "sha256:7d73d2a99753107a36ac6b455ee49046802e59d9d076ef8e47b61499fa29afff",
+                "sha256:e96da0d330793e2cb9485e9ddfd918d456036c7149416295932478192f4436a1"
+            ],
+            "markers": "python_version != '3.4'",
+            "version": "==0.4.3"
+        },
+        "conllu": {
+            "hashes": [
+                "sha256:271dac1d2f82b6ecafaf5002835daab81721c3b01edfc034ebcf0bf53c122c4a",
+                "sha256:46c7b05de6582476648f9939fc5215ed206917f43117f0f7b6bb756f9bc95899"
+            ],
+            "version": "==2.2.2"
+        },
         "cymem": {
             "hashes": [
-                "sha256:081c652ae1aff4759813e93a2fc4df4ba410ce214a0e542988e24c62110d4cd0",
-                "sha256:0e447fa4cb6dccd0b96257a798370a17bef3ec254a527230058e41816a777c04",
-                "sha256:2c8267dcb15cc6ab318f01ceaf16b8440c0386ae44014d5b22fefe5b0398d05c",
-                "sha256:46141111eedbb5b0d8c9386b00226a15f5727a1202b9095f4363d425f259267e",
-                "sha256:4994c1f3e948bd58a6e38c905221680563b851983a15f1f01e5ff415d560d153",
-                "sha256:584872fd3df176e50c90e37aaca6cb731ac0abcdea4f5b8ad77c30674cfaaa99",
-                "sha256:6e3194135b21bb268030f3473beb8b674b356c330a9fa185dced2f5006cbd5ba",
-                "sha256:71710ee0e946a6bd33c86dd9e71f95ad584c65e8bb02615f00ceb0d8348fb303",
-                "sha256:741957f541fb8322de5a8c711d5d58f80d684225d2aec32fec92484cac931a52",
-                "sha256:7f01ba6153427811cd7d35630081c69b32c188a1d330599a826ef3bf17edbd7c",
-                "sha256:8d96e95902e781950d7c255b19364a1ed50a204843d63dd386b0abc5e6df5e44",
-                "sha256:8dd169ece1629ec4db1a592321e3ae0a9bb62fda2052a351fc36871f314c3569",
-                "sha256:8e6ad29636edd559b0dfe0a19c5cb5e6257461a5df90839e8c7710ddb005f4b4",
-                "sha256:9935b233882732f03fd0fadbeb9e9aa672edcdd126e6d52c36d60adf1def8ea5",
-                "sha256:a38b3229782411e4b23240f5f90000c4e7a834af88ed8763c66f8e4603db6b51",
-                "sha256:a5966b3171bad9c84a2b19dccda5ab37ae8437c0709a6b72cb42b64ea76a4bd3",
-                "sha256:ab88b1534f06df07262d9bc5efb3ba07948cdbe9a363eb9eaa4ad42fae6c7b5e",
-                "sha256:b08b0dd7adafbff9f0fd7dc8dcad5f3ce6f23c126c81ad8d1666880cc94e6974",
-                "sha256:ba47b571d480c0b76d282ff1634372070031d4998a46ae5d8305d49563b74ca6",
-                "sha256:bf049dc9cf0d3aa4a48ba514b7f1699fb6f35b18ad8c6f018bd13e0bccd9d30c",
-                "sha256:c46a122c524a3270ac5249f590ac2f75f1a83692a3d3a03479cea49de72a0a89",
-                "sha256:c63337aa7e1ad4ec182cc7847c6d85390589fbbf1f9f67d1fde8133a9acb7fa8",
-                "sha256:ec51273ea08a2c6389bc4dd6b5183354826d916b149a041f2f274431166191bc"
-            ],
-            "version": "==2.0.2"
+                "sha256:5083b2ab5fe13ced094a82e0df465e2dbbd9b1c013288888035e24fd6eb4ed01",
+                "sha256:622c20a57701d02f01a47e856dea248e112638f28c8249dbe3ed95a9702e3d74",
+                "sha256:6f4cb689a9552e9e13dccc89203c8ab09f210a7ffb92ce27c384a4a0be27b527",
+                "sha256:719f04a11ca709fc2b47868070d79fccff77e5d502ff32de2f4baa73cb16166f",
+                "sha256:7236252bed70f37b898933dcf8aa875d0829664a245a272516f27b30439df71c",
+                "sha256:7f5ddceb12b73f7fd2e4398266401b6f887003740ccd18c989a2af04500b5f2b",
+                "sha256:85b9364e099426bd7f445a7705aad87bf6dbb71d79e3802dd8ca14e181d38a33",
+                "sha256:c288a1bbdf58c360457443e5297e74844e1961e5e7001dbcb3a5297a41911a11",
+                "sha256:cd21ec48ee70878d46c486e2f7ae94b32bfc6b37c4d27876c5a5a00c4eb75c3c",
+                "sha256:d7505c500d994f11662e5595f5002251f572acc189f18944619352e2636f5181",
+                "sha256:dd24848fbd75b17bab06408da6c029ba7cc615bd9e4a1f755fb3a090025fb922",
+                "sha256:f4f19af4bca81f11922508a9dcf30ce1d2aee4972af9f81ce8e5331a6f46f5e1"
+            ],
+            "version": "==2.0.3"
         },
         "decorator": {
             "hashes": [
@@ -83,12 +125,49 @@
             ],
             "version": "==4.4.1"
         },
+        "docutils": {
+            "hashes": [
+                "sha256:6c4f696463b79f1fb8ba0c594b63840ebd41f059e92b31957c46b74a4599b6d0",
+                "sha256:9e4d7ecfc600058e07ba661411a2b7de2fd0fafa17d1a7f7361cd47b1175c827",
+                "sha256:a2aeea129088da402665e92e0b25b04b073c04b2dce4ab65caaa38b7ce2e1a99"
+            ],
+            "version": "==0.15.2"
+        },
         "fire": {
             "hashes": [
                 "sha256:6865fefc6981a713d2ce56a2a2c92c56c729269f74a6cddd6f4b94d16ae084c9"
             ],
             "version": "==0.2.1"
         },
+        "gensim": {
+            "hashes": [
+                "sha256:10d0a01a541aa9810add46fe2ccb043a86d99f5075f8dc35466f65949adcb64c",
+                "sha256:11c01f38a77579b4fda635b92f1231b2986d9fefff44af401c2d106901b9342a",
+                "sha256:1a302a0829eaa023fa6e91e3b489e5b6173127124f2bfbdfa4118b75bd402264",
+                "sha256:1b747baf65b51e101db5b4b8eb84ac2c400ab81c3fb0758ff5f879693e936ce3",
+                "sha256:1cfd541f4a8651d5d24f61b35a263573bb3464f1d72d12366c4ab15931c81fad",
+                "sha256:267983deeb6969a43f7366a188c4f5f2942a49271b7dbdfc07066707fb2029e2",
+                "sha256:33277fc0a8d7b0c7ce70fcc74bb82ad39f944c009b334856c6e86bf552b1dfdc",
+                "sha256:36518200175c708e762f5763f34b1e5a6b64e7844f2c172afd480231361192fd",
+                "sha256:458c2dea186e7e35fa417b13060c4c5ce63e90c36240c745574a95c2b02c425b",
+                "sha256:498bc173df0cebb430e01d9860d8cb9a1d4724537da3b3b1b6ec371ddd86794e",
+                "sha256:4e5344a07e6646198669befb3f6d3d25209693a904863293da2b0233c5526686",
+                "sha256:56fc56f1b8044f33c29ab3f5416d05a1655223a7654cc0887e4e0a17f50e6006",
+                "sha256:58f28f6dd25cb4f83ace8b6be1e132d56acfd60b823241e84bb8cba2e9676a39",
+                "sha256:6c7065de589edd53cd42f4e5bbae8a821d285c75f12412ee0e32c972f14672c1",
+                "sha256:734c8fcd65068f06c1da37beeec7b22a9380faa63a307a99b0bb109560e05e47",
+                "sha256:afca1f0081b5e1ce3f07d7ac540196c1af81b9d0513fbd8c4744cc7910922e09",
+                "sha256:b08c526ce5dabceb042499c7711e229fb3d2874f69a83dbcab77a35bedac8552",
+                "sha256:ba19d8021554c773420321cc0b1ad5ed1f0cd5d14c08a24631263f23d6e8c14e",
+                "sha256:bd0e40149a6f07961123f8770b68050366b6772f38521c75a9d0b03242cb9fde",
+                "sha256:d3b3cb01e4e997731c41944ffbfce2af67585c6a60fd4fc4a02909dff77021aa",
+                "sha256:d9f13b7f0c425b632f18b0b7c87551a0e0d87695506e63153b442e137a4a69fd",
+                "sha256:db3b8cfc03e9b7e4c93ea2221cb82d9719fcc3cd16201191f45f12e3c9b0f206",
+                "sha256:ef51a5aada90ce0f8f13be750e8d8460bd33e2793c2e74bb453fb1ce7b7ba79d"
+            ],
+            "index": "pypi",
+            "version": "==3.8.1"
+        },
         "idna": {
             "hashes": [
                 "sha256:c357b3f628cf53ae2c4c05627ecc484553142ca23264e593d327bcde5e9c3407",
@@ -98,18 +177,25 @@
         },
         "importlib-metadata": {
             "hashes": [
-                "sha256:aa18d7378b00b40847790e7c27e11673d7fed219354109d0e7b9e5b25dc3ad26",
-                "sha256:d5f18a79777f3aa179c145737780282e27b508fc8fd688cb17c7a813e8bd39af"
+                "sha256:06f5b3a99029c7134207dd882428a66992a9de2bef7c2b699b5641f9886c3302",
+                "sha256:b97607a1a18a5100839aec1dc26a1ea17ee0d93b20b0f008d80a5a050afb200b"
             ],
             "markers": "python_version < '3.8'",
-            "version": "==0.23"
+            "version": "==1.5.0"
         },
-        "more-itertools": {
+        "jmespath": {
+            "hashes": [
+                "sha256:3720a4b1bd659dd2eecad0666459b9788813e032b83e7ba58578e48254e0a0e6",
+                "sha256:bde2aef6f44302dfb30320115b17d030798de8c4110e28d5cf6cf91a7a31074c"
+            ],
+            "version": "==0.9.4"
+        },
+        "joblib": {
             "hashes": [
-                "sha256:409cd48d4db7052af495b09dec721011634af3753ae1ef92d2b32f73a745f832",
-                "sha256:92b8c4b06dac4f0611c0729b2f2ede52b2e1bac1ab48f089c7ddc12e26bb60c4"
+                "sha256:0630eea4f5664c463f23fbf5dcfc54a2bc6168902719fa8e19daf033022786c8",
+                "sha256:bdb4fd9b72915ffb49fde2229ce482dd7ae79d842ed8c2b4c932441495af1403"
             ],
-            "version": "==7.2.0"
+            "version": "==0.14.1"
         },
         "murmurhash": {
             "hashes": [
@@ -119,22 +205,33 @@
                 "sha256:3af36a0dc9f13f6892d9b8b39a6a3ccf216cae5bce38adc7c2d145677987772f",
                 "sha256:717196a04cdc80cc3103a3da17b2415a8a5e1d0d578b7079259386bf153b3258",
                 "sha256:8a4ed95cd3456b43ea301679c7c39ade43fc18b844b37d0ba0ac0d6acbff8e0c",
+                "sha256:8b045a79e8b621b4b35b29f29e33e9e0964f3a276f7da4d5736142f322ad4842",
                 "sha256:a6c071b4b498bcea16a8dc8590cad81fa8d43821f34c74bc00f96499e2527073",
                 "sha256:b0afe329701b59d02e56bc6cee7325af83e3fee9c299c615fc1df3202b4f886f",
                 "sha256:ba766343bdbcb928039b8fff609e80ae7a5fd5ed7a4fc5af822224b63e0cbaff",
                 "sha256:bf33490514d308bcc27ed240cb3eb114f1ec31af031535cd8f27659a7049bd52",
                 "sha256:c7a646f6b07b033642b4f52ae2e45efd8b80780b3b90e8092a0cec935fbf81e2",
+                "sha256:cc97ea766ac545074bab0e5af3dbc48e0d05ba230ae5a404e284d39abe4b3baf",
                 "sha256:d696c394ebd164ca80b5871e2e9ad2f9fdbb81bd3c552c1d5f1e8ee694e6204a",
+                "sha256:f468e4868f78c3ac202a66abfe2866414bca4ae7666a21ef0938c423de0f7d50",
                 "sha256:fe344face8d30a5a6aa26e5acf288aa2a8f0f32e05efdda3d314b4bf289ec2af"
             ],
             "version": "==1.0.2"
         },
+        "negspacy": {
+            "hashes": [
+                "sha256:f7d5834ce677bbc7854f3a52dc40774ed0864d22e5b466b3c8860a131b3e4364"
+            ],
+            "index": "pypi",
+            "version": "==0.1.6"
+        },
         "networkx": {
             "hashes": [
-                "sha256:8311ddef63cf5c5c5e7c1d0212dd141d9a1fe3f474915281b73597ed5f1d4e3d"
+                "sha256:cdfbf698749a5014bf2ed9db4a07a5295df1d3a53bf80bf3cbd61edf9df05fa1",
+                "sha256:f8f4ff0b6f96e4f9b16af6b84622597b5334bf9cae8cf9b2e42e7985d5c95c64"
             ],
             "index": "pypi",
-            "version": "==2.3"
+            "version": "==2.4"
         },
         "nltk": {
             "hashes": [
@@ -143,31 +240,63 @@
             "index": "pypi",
             "version": "==3.4.5"
         },
+        "nmslib": {
+            "hashes": [
+                "sha256:08d68095cc16b466bcf5f4655d371b717caadb47afb157f664b7f47a7f96a20e",
+                "sha256:0ffa3765625c5942c68488d4fe07c96fdc1626eb205966fd7adff4705e1644ea",
+                "sha256:26695bd4ae9714f53635168376bb7b9a8c3158c5252a8f9fbaa3317e24f1c018",
+                "sha256:2cc1002b6b919c3fd31975b7a4af8aec44b02bf13431e9be00af32f92986e055",
+                "sha256:2d5c565aaffca02cbdc6f0fddcba99a645b36f75971c0883d646c63a37782d57",
+                "sha256:32d245100cdd43879fc8d8aba5a6ea1b6d5c69640af08bc9fc45a4e3b4d77113",
+                "sha256:393f5b9ea7d8fed2a560293d41875296de0afe070a5f217636c593c94cc3d21d",
+                "sha256:42a21bc92ba22ce9042256af7c0e8f2383aeb6fac6ad1e4e68fb86b84ff947d5",
+                "sha256:5f021ecce22b1733dc3d4abd576b9636986d4d205acf1bbb2d4fb21074f3ea2a",
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@@ -177,36 +306,30 @@
         },
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         "phenopy": {
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-            "version": "==0.2.1"
+            "version": "==0.3.2"
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@@ -218,6 +341,7 @@
         "preshed": {
             "hashes": [
                 "sha256:0c15ae62f2595ca479decc3452967484dae57b510278800f5deb9115238cc818",
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@@ -229,16 +353,54 @@
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                 "sha256:8a9a8222a697a513f25a94733e7a17cc298ecd8fd56b606a1d8fa0ac342c2830",
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             "version": "==3.0.2"
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         "python-dateutil": {
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-            "version": "==2.8.0"
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         },
         "pytz": {
             "hashes": [
@@ -247,6 +409,22 @@
             ],
             "version": "==2019.3"
         },
+        "pyyaml": {
+            "hashes": [
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+                "sha256:e4c015484ff0ff197564917b4b4246ca03f411b9bd7f16e02a2f586eb48b6d04",
+                "sha256:ebc4ed52dcc93eeebeae5cf5deb2ae4347b3a81c3fa12b0b8c976544829396a4"
+            ],
+            "version": "==5.2"
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                 "sha256:11e007a8a2aa0323f5a921e9e6a2d7e4e67d9877e85773fba9ba6419025cbeb4",
@@ -254,45 +432,126 @@
             ],
             "version": "==2.22.0"
         },
+        "rsa": {
+            "hashes": [
+                "sha256:25df4e10c263fb88b5ace923dd84bf9aa7f5019687b5e55382ffcdb8bede9db5",
+                "sha256:43f682fea81c452c98d09fc316aae12de6d30c4b5c84226642cf8f8fd1c93abd"
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+            "version": "==3.4.2"
+        },
+        "s3transfer": {
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+        },
+        "scikit-learn": {
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             "index": "pypi",
-            "version": "==2.2.2"
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-            "version": "==0.2.0"
+                "sha256:1102b4984f9f56364540e47d83fac3e7543903dfbb92f0d0e5dd3bfd40528934",
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+                "sha256:c6bdf53a87770139c6a9d75b3e664505bd81c022312fafca35ed38714e4ecdf1",
+                "sha256:c82e6dc3727454edc6ccdb1d07d5bc0aab3f43539fb8d9f973cf769135d2c7e4",
+                "sha256:ca1ec20ea6e14ad56ccaa84aa6c79d6e51fccf32e0040372b4d06c6e5dbb7fee",
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+                "sha256:de329ba0ff451308d59e40c39372f5231e7c364f4933d7457788203630bdede2"
+            ],
+            "version": "==1.0.1"
         },
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@@ -303,6 +562,7 @@
         "thinc": {
             "hashes": [
                 "sha256:1dbaec0628040a1f8d66147fadbf7775ad6dfe4c681424b2e20479c1e54dc3c1",
+                "sha256:20b6ed4a8112342b433b9b3ca23b59322d07e32a9232d3cca19b0353e213eadb",
                 "sha256:30790a1a496a8a84fe300edf50df50454dbdb625b41b203739fbc03112a4d3b6",
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                 "sha256:650fbead603bd7e73a61fd2c1b69202ad7a8eb70d4ebe7c5484b8788e828b6e0",
@@ -310,48 +570,44 @@
                 "sha256:801f32f6c048de7e9f6d406342080e6348d0bb02beb1412811f9150a26661691",
                 "sha256:8833246f1c8b95143c91e310728bf64af8972a9d8653252efa1b4c9036837569",
                 "sha256:93cb9d184115a8890321dd7f5d94a0d8235dc2fca54d92a9c1c051234a7af43e",
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                 "sha256:bad16bcc608ec4d74c680d85aa9bf43cfc776ac12ca3b7e699d7283fd0177bca",
-                "sha256:ce81d6b2372057e10f9d7cb505942df67a803f270d69959d44d372e8e3792bb9"
+                "sha256:ce81d6b2372057e10f9d7cb505942df67a803f270d69959d44d372e8e3792bb9",
+                "sha256:f19a36cdfdbef75109f505313c16a7b154b9bbf83dd177e9ddd43430dc523bb0"
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             "version": "==7.3.1"
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         "tqdm": {
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-                "sha256:fca09992116d6dc3ad9789cf601a254081eb40d5c14c1863ab6cd10e15c2cb26"
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-            "version": "==4.37.0"
+            "version": "==4.42.1"
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         "urllib3": {
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-                "sha256:9a107b99a5393caf59c7aa3c1249c16e6879447533d0887f4336dde834c7be86"
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-            "version": "==1.25.6"
+            "markers": "python_version != '3.4'",
+            "version": "==1.25.8"
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         "wasabi": {
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-                "sha256:7127b5f75d0f17f51048ca81d62440655ae87afcbed36adad717a5e35e671fa8"
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-            "version": "==0.3.0"
+            "version": "==0.6.0"
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-                "sha256:f06903e9f1f43b12d371004b4ac7b06ab39a44adc747266928ae6debfa7b3335"
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-            "version": "==0.6.0"
+            "version": "==3.0.0"
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     "develop": {
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                 "sha256:08a96c641c3a74e44eb59afb61a24f2cb9f4d7188748e76ba4bb5edfa3cb7d1c",
@@ -361,40 +617,39 @@
         },
         "coverage": {
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+                "sha256:25dbf1110d70bab68a74b4b9d74f30e99b177cde3388e07cc7272f2168bd1477",
+                "sha256:3230d1003eec018ad4a472d254991e34241e0bbd513e97a29727c7c2f637bd2a",
+                "sha256:3dbb72eaeea5763676a1a1efd9b427a048c97c39ed92e13336e726117d0b72bf",
+                "sha256:5012d3b8d5a500834783689a5d2292fe06ec75dc86ee1ccdad04b6f5bf231691",
+                "sha256:51bc7710b13a2ae0c726f69756cf7ffd4362f4ac36546e243136187cfcc8aa73",
+                "sha256:527b4f316e6bf7755082a783726da20671a0cc388b786a64417780b90565b987",
+                "sha256:722e4557c8039aad9592c6a4213db75da08c2cd9945320220634f637251c3894",
+                "sha256:76e2057e8ffba5472fd28a3a010431fd9e928885ff480cb278877c6e9943cc2e",
+                "sha256:77afca04240c40450c331fa796b3eab6f1e15c5ecf8bf2b8bee9706cd5452fef",
+                "sha256:7afad9835e7a651d3551eab18cbc0fdb888f0a6136169fbef0662d9cdc9987cf",
+                "sha256:9bea19ac2f08672636350f203db89382121c9c2ade85d945953ef3c8cf9d2a68",
+                "sha256:a8b8ac7876bc3598e43e2603f772d2353d9931709345ad6c1149009fd1bc81b8",
+                "sha256:b0840b45187699affd4c6588286d429cd79a99d509fe3de0f209594669bb0954",
+                "sha256:b26aaf69713e5674efbde4d728fb7124e429c9466aeaf5f4a7e9e699b12c9fe2",
+                "sha256:b63dd43f455ba878e5e9f80ba4f748c0a2156dde6e0e6e690310e24d6e8caf40",
+                "sha256:be18f4ae5a9e46edae3f329de2191747966a34a3d93046dbdf897319923923bc",
+                "sha256:c312e57847db2526bc92b9bfa78266bfbaabac3fdcd751df4d062cd4c23e46dc",
+                "sha256:c60097190fe9dc2b329a0eb03393e2e0829156a589bd732e70794c0dd804258e",
+                "sha256:c62a2143e1313944bf4a5ab34fd3b4be15367a02e9478b0ce800cb510e3bbb9d",
+                "sha256:cc1109f54a14d940b8512ee9f1c3975c181bbb200306c6d8b87d93376538782f",
+                "sha256:cd60f507c125ac0ad83f05803063bed27e50fa903b9c2cfee3f8a6867ca600fc",
+                "sha256:d513cc3db248e566e07a0da99c230aca3556d9b09ed02f420664e2da97eac301",
+                "sha256:d649dc0bcace6fcdb446ae02b98798a856593b19b637c1b9af8edadf2b150bea",
+                "sha256:d7008a6796095a79544f4da1ee49418901961c97ca9e9d44904205ff7d6aa8cb",
+                "sha256:da93027835164b8223e8e5af2cf902a4c80ed93cb0909417234f4a9df3bcd9af",
+                "sha256:e69215621707119c6baf99bda014a45b999d37602cb7043d943c76a59b05bf52",
+                "sha256:ea9525e0fef2de9208250d6c5aeeee0138921057cd67fcef90fbed49c4d62d37",
+                "sha256:fca1669d464f0c9831fd10be2eef6b86f5ebd76c724d1e0706ebdff86bb4adf0"
+            ],
+            "version": "==5.0.3"
         },
         "entrypoints": {
             "hashes": [
@@ -413,11 +668,11 @@
         },
         "importlib-metadata": {
             "hashes": [
-                "sha256:aa18d7378b00b40847790e7c27e11673d7fed219354109d0e7b9e5b25dc3ad26",
-                "sha256:d5f18a79777f3aa179c145737780282e27b508fc8fd688cb17c7a813e8bd39af"
+                "sha256:06f5b3a99029c7134207dd882428a66992a9de2bef7c2b699b5641f9886c3302",
+                "sha256:b97607a1a18a5100839aec1dc26a1ea17ee0d93b20b0f008d80a5a050afb200b"
             ],
             "markers": "python_version < '3.8'",
-            "version": "==0.23"
+            "version": "==1.5.0"
         },
         "mccabe": {
             "hashes": [
@@ -428,31 +683,31 @@
         },
         "more-itertools": {
             "hashes": [
-                "sha256:409cd48d4db7052af495b09dec721011634af3753ae1ef92d2b32f73a745f832",
-                "sha256:92b8c4b06dac4f0611c0729b2f2ede52b2e1bac1ab48f089c7ddc12e26bb60c4"
+                "sha256:5dd8bcf33e5f9513ffa06d5ad33d78f31e1931ac9a18f33d37e77a180d393a7c",
+                "sha256:b1ddb932186d8a6ac451e1d95844b382f55e12686d51ca0c68b6f61f2ab7a507"
             ],
-            "version": "==7.2.0"
+            "version": "==8.2.0"
         },
         "packaging": {
             "hashes": [
-                "sha256:28b924174df7a2fa32c1953825ff29c61e2f5e082343165438812f00d3a7fc47",
-                "sha256:d9551545c6d761f3def1677baf08ab2a3ca17c56879e70fecba2fc4dde4ed108"
+                "sha256:170748228214b70b672c581a3dd610ee51f733018650740e98c7df862a583f73",
+                "sha256:e665345f9eef0c621aa0bf2f8d78cf6d21904eef16a93f020240b704a57f1334"
             ],
-            "version": "==19.2"
+            "version": "==20.1"
         },
         "pluggy": {
             "hashes": [
-                "sha256:0db4b7601aae1d35b4a033282da476845aa19185c1e6964b25cf324b5e4ec3e6",
-                "sha256:fa5fa1622fa6dd5c030e9cad086fa19ef6a0cf6d7a2d12318e10cb49d6d68f34"
+                "sha256:15b2acde666561e1298d71b523007ed7364de07029219b604cf808bfa1c765b0",
+                "sha256:966c145cd83c96502c3c3868f50408687b38434af77734af1e9ca461a4081d2d"
             ],
-            "version": "==0.13.0"
+            "version": "==0.13.1"
         },
         "py": {
             "hashes": [
-                "sha256:64f65755aee5b381cea27766a3a147c3f15b9b6b9ac88676de66ba2ae36793fa",
-                "sha256:dc639b046a6e2cff5bbe40194ad65936d6ba360b52b3c3fe1d08a82dd50b5e53"
+                "sha256:5e27081401262157467ad6e7f851b7aa402c5852dbcb3dae06768434de5752aa",
+                "sha256:c20fdd83a5dbc0af9efd622bee9a5564e278f6380fffcacc43ba6f43db2813b0"
             ],
-            "version": "==1.8.0"
+            "version": "==1.8.1"
         },
         "pycodestyle": {
             "hashes": [
@@ -470,18 +725,18 @@
         },
         "pyparsing": {
             "hashes": [
-                "sha256:6f98a7b9397e206d78cc01df10131398f1c8b8510a2f4d97d9abd82e1aacdd80",
-                "sha256:d9338df12903bbf5d65a0e4e87c2161968b10d2e489652bb47001d82a9b028b4"
+                "sha256:4c830582a84fb022400b85429791bc551f1f4871c33f23e44f353119e92f969f",
+                "sha256:c342dccb5250c08d45fd6f8b4a559613ca603b57498511740e65cd11a2e7dcec"
             ],
-            "version": "==2.4.2"
+            "version": "==2.4.6"
         },
         "pytest": {
             "hashes": [
-                "sha256:27abc3fef618a01bebb1f0d6d303d2816a99aa87a5968ebc32fe971be91eb1e6",
-                "sha256:58cee9e09242937e136dbb3dab466116ba20d6b7828c7620f23947f37eb4dae4"
+                "sha256:0d5fe9189a148acc3c3eb2ac8e1ac0742cb7618c084f3d228baaec0c254b318d",
+                "sha256:ff615c761e25eb25df19edddc0b970302d2a9091fbce0e7213298d85fb61fef6"
             ],
             "index": "pypi",
-            "version": "==5.2.2"
+            "version": "==5.3.5"
         },
         "pytest-cov": {
             "hashes": [
@@ -493,24 +748,24 @@
         },
         "six": {
             "hashes": [
-                "sha256:3350809f0555b11f552448330d0b52d5f24c91a322ea4a15ef22629740f3761c",
-                "sha256:d16a0141ec1a18405cd4ce8b4613101da75da0e9a7aec5bdd4fa804d0e0eba73"
+                "sha256:236bdbdce46e6e6a3d61a337c0f8b763ca1e8717c03b369e87a7ec7ce1319c0a",
+                "sha256:8f3cd2e254d8f793e7f3d6d9df77b92252b52637291d0f0da013c76ea2724b6c"
             ],
-            "version": "==1.12.0"
+            "version": "==1.14.0"
         },
         "wcwidth": {
             "hashes": [
-                "sha256:3df37372226d6e63e1b1e1eda15c594bca98a22d33a23832a90998faa96bc65e",
-                "sha256:f4ebe71925af7b40a864553f761ed559b43544f8f71746c2d756c7fe788ade7c"
+                "sha256:8fd29383f539be45b20bd4df0dc29c20ba48654a41e661925e612311e9f3c603",
+                "sha256:f28b3e8a6483e5d49e7f8949ac1a78314e740333ae305b4ba5defd3e74fb37a8"
             ],
-            "version": "==0.1.7"
+            "version": "==0.1.8"
         },
         "zipp": {
             "hashes": [
-                "sha256:3718b1cbcd963c7d4c5511a8240812904164b7f381b647143a89d3b98f9bcd8e",
-                "sha256:f06903e9f1f43b12d371004b4ac7b06ab39a44adc747266928ae6debfa7b3335"
+                "sha256:12248a63bbdf7548f89cb4c7cda4681e537031eda29c02ea29674bc6854460c2",
+                "sha256:7c0f8e91abc0dc07a5068f315c52cb30c66bfbc581e5b50704c8a2f6ebae794a"
             ],
-            "version": "==0.6.0"
+            "version": "==3.0.0"
         }
     }
 }
diff --git a/README.md b/README.md
index 77f316a..0d7bafa 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,9 @@
 # txt2hpo
 `txt2hpo` is a Python library for extracting HPO-encoded phenotypes from text.
-`txt2hpo` recognizes differences in inflection (e.g. hypotonic vs. hypotonia), is able to parse complex multi-word phenotypes with differing word order (e.g. developmentally delayed vs. developmental delay) and comes with a built-in medical spellchecker. 
+`txt2hpo` recognizes differences in inflection (e.g. hypotonic vs. hypotonia), handles negation and comes with a built-in medical spellchecker. 
 
 # Installation
 
-Install using pip:
-```bash
-pip install txt2hpo
-```
-
 Install from GitHub
 ```bash
 
@@ -21,38 +16,85 @@ python setup.py install
 # Library usage
 
 ```python 
-from txt2hpo.extract import hpo
+from txt2hpo.extract import Extractor
+extract = Extractor()
+
+result = extract.hpo("patient with developmental delay and hypotonia")
 
-hpo_ids = hpo("patient with developmental delay and hypotonia")
+print(result.hpids)
 
-print(hpo_ids)
 
-[{"hpid": ["HP:0001290"], "index": [37, 46], "matched": "hypotonia"}, 
- {"hpid": ["HP:0001263"], "index": [13, 32], "matched": "developmental delay"}]
+["HP:0001263", "HP:0001290"]
     
 ```
 
-`txt2hpo` will attempt to correct spelling errors by default, at the cost of slower processing speed.
+`txt2hpo` will attempt to correct spelling errors by default, at the cost of slower processing.
 This feature can be turned off by setting the `correct_spelling` flag to `False`. 
 
 ```python 
-from txt2hpo.extract import hpo
+from txt2hpo.extract import Extractor
+extract = Extractor(correct_spelling = False)
+result = extract.hpo("patient with devlopental delay and hyptonia")
+
+print(result.hpids)
+
+[]
+ 
+    
+```
+
+`txt2hpo` handles negation using [negspaCy](https://spacy.io/universe/project/negspacy). To remove negated phenotypes set `remove_negated` flag to True.
+ 
 
-hpo_ids = hpo("patient with devlopental delay and hyptonia", correct_spelling=True)
+```python 
+from txt2hpo.extract import Extractor
+extract = Extractor(remove_negated=True)
+result = extract.hpo("patient has developmental delay but no hypotonia")
 
-print(hpo_ids)
+print(result.hpids)
 
-[{"hpid": ["HP:0001290"], "index": [37, 46], "matched": "hypotonia"}, 
- {"hpid": ["HP:0001263"], "index": [13, 32], "matched": "developmental delay"}]
+["HP:0001263"]
+ 
     
 ```
 
-`txt2hpo` outputs a valid JSON string, use`json.loads` to convert `txt2hpo` output to JSON/python list.
+`txt2hpo` picks the longest overlapping phenotype by default. To disable this feature set `remove_overlapping` flag to False.
+ 
+
+```python 
+from txt2hpo.extract import Extractor
+extract = Extractor(remove_overlapping=False)
+result = extract.hpo("patient with polycystic kidney disease")
+
+print(result.hpids)
+
+["HP:0000113", "HP:0000112"]
 
-```python
-import json
-from txt2hpo.extract import hpo
-hpo_ids = json.loads(hpo(text_string))
+
+extract = Extractor(remove_overlapping=True)
+result = extract.hpo("patient with polycystic kidney disease")
+
+print(result.hpids)
+
+["HP:0000113"]
+ 
+    
 ```
 
+`txt2hpo` outputs a valid JSON string, which contains information about extracted HPIDs, their character span and matched string.
+
+```python 
+from txt2hpo.extract import Extractor
+extract = Extractor()
+
+result = extract.hpo("patient with developmental delay and hypotonia")
+
+print(result.json)
+
+
+'[{"hpid": ["HP:0001290"], "index": [37, 46], "matched": "hypotonia"}, 
+{"hpid": ["HP:0001263"], "index": [13, 32], "matched": "developmental delay"}]'
+
+    
+```
 
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..c3e6928
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1 @@
+https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.2.4/en_core_sci_sm-0.2.4.tar.gz#egg=en_core_sci_sm
diff --git a/setup.py b/setup.py
index 6ab5013..f03e980 100644
--- a/setup.py
+++ b/setup.py
@@ -11,7 +11,7 @@
 name=__project__,
     packages=find_packages(),
     version=__version__,
-    description='Phenotype extraction using Named Entity Recognition',
+    description='HPO concept recognition and phenotype extraction tool',
     long_description=long_description,
     long_description_content_type='text/markdown',
     author='Vlad Gainullin <vgainullin@genedx.com>, Kevin Arvai <karvai@genedx.com>',
@@ -27,7 +27,13 @@
         'pandas',
         'nltk',
         'spacy',
-        'networkx'
+        'scispacy',
+        'negspacy',
+        'networkx',
+        'gensim',
+        'en_core_sci_sm'
 
-    ]
+
+    ],
+    dependency_links=['https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.2.4/en_core_sci_sm-0.2.4.tar.gz#egg=en_core_sci_sm']
 )
diff --git a/tests/test_build_tree.py b/tests/test_build_tree.py
new file mode 100644
index 0000000..b798dea
--- /dev/null
+++ b/tests/test_build_tree.py
@@ -0,0 +1,19 @@
+import unittest
+import time
+
+from txt2hpo.build_tree import build_search_tree
+
+
+class BuildTreeTestCase(unittest.TestCase):
+    def setUp(self):
+        self.startTime = time.time()
+
+    def tearDown(self):
+        t = time.time() - self.startTime
+        print('%s: %.3f' % (self.id(), t))
+
+    def test_build_search_tree(self):
+        custom_synonyms = {"HP:0001263": ['DD', 'GDD']}
+        search_tree = build_search_tree(custom_synonyms)
+        self.assertEqual(search_tree['dd'], {1: {'dd': ['HP:0001263']}})
+        self.assertEqual(search_tree['gdd'], {1: {'gdd': ['HP:0001263']}})
diff --git a/tests/test_cases.py b/tests/test_cases.py
new file mode 100644
index 0000000..ca12a52
--- /dev/null
+++ b/tests/test_cases.py
@@ -0,0 +1,124 @@
+# Phenotype-distance/coocurance test cases
+
+test_case0 = """[{"hpid": ["HP:0001290"], "index": [895, 904], "matched": "hypotonia"}, 
+             {"hpid": ["HP:0000218"], "index": [3184, 3195], "matched": "high palate"}]"""
+
+test_case1 = """[{"hpid": ["HP:0001290"], "index": [895, 904], "matched": "hypotonia"}, 
+              {"hpid": ["HP:0002014"], "index": [1597, 1605], "matched": "diarrhea"}, 
+             {"hpid": ["HP:0000218"], "index": [3184, 3195], "matched": "high palate"}]"""
+
+test_case2 = """[{"hpid": ["HP:0001290"], "index": [895, 904], "matched": "hypotonia"},
+              {"hpid": ["HP:0001290"], "index": [1095, 1104], "matched": "hypotonia"},
+              {"hpid": ["HP:0002014"], "index": [1597, 1605], "matched": "diarrhea"}, 
+             {"hpid": ["HP:0000218"], "index": [3184, 3195], "matched": "high palate"}]"""
+
+
+test_case10 = """[{"hpid": ["HP:0001290"], "index": [895, 904], "matched": "hypotonia"},
+ {"hpid": ["HP:0001250"], "index": [912, 920], "matched": "epilepsy"},
+ {"hpid": ["HP:0000717"], "index": [1143, 1149], "matched": "autism"},
+ {"hpid": ["HP:0000729", "HP:0001631"],
+  "index": [1169, 1172],
+  "matched": "ASD"},
+ {"hpid": ["HP:0000752"], "index": [1234, 1247], "matched": "hyperactivity"},
+ {"hpid": ["HP:0011856"], "index": [1418, 1422], "matched": "pica"},
+ {"hpid": ["HP:0000718"], "index": [1433, 1443], "matched": "aggression"},
+ {"hpid": ["HP:0000739"], "index": [1480, 1487], "matched": "anxiety"},
+ {"hpid": ["HP:0002019"], "index": [1569, 1581], "matched": "constipation"},
+ {"hpid": ["HP:0002014"], "index": [1597, 1605], "matched": "diarrhea"},
+ {"hpid": ["HP:0000486"], "index": [1701, 1711], "matched": "strabismus"},
+ {"hpid": ["HP:0002650"], "index": [1819, 1828], "matched": "scoliosis"},
+ {"hpid": ["HP:0001371"], "index": [1836, 1848], "matched": "contractures"},
+ {"hpid": ["HP:0000998"], "index": [1900, 1914], "matched": "hypertrichosis"},
+ {"hpid": ["HP:0001363"],
+  "index": [2060, 2076],
+  "matched": "craniosynostosis"},
+ {"hpid": ["HP:0001363"],
+  "index": [2231, 2247],
+  "matched": "craniosynostosis"},
+ {"hpid": ["HP:0001363"],
+  "index": [2385, 2401],
+  "matched": "craniosynostosis"},
+ {"hpid": ["HP:0000252"], "index": [2495, 2507], "matched": "microcephaly"},
+ {"hpid": ["HP:0025502"], "index": [2638, 2648], "matched": "overweight"},
+ {"hpid": ["HP:0000581"],
+  "index": [2935, 2951],
+  "matched": "blepharophimosis"},
+ {"hpid": ["HP:0000506"], "index": [2959, 2970], "matched": "telecanthus"},
+ {"hpid": ["HP:0008551"], "index": [3203, 3211], "matched": "microtia"},
+ {"hpid": ["HP:0000508"], "index": [3282, 3288], "matched": "ptosis"},
+ {"hpid": ["HP:0001270"], "index": [609, 620], "matched": "motor delay"},
+ {"hpid": ["HP:0000729"],
+  "index": [1143, 1167],
+  "matched": "autism spectrum disorder"},
+ {"hpid": ["HP:0000736"],
+  "index": [1391, 1416],
+  "matched": "were short attention span"},
+ {"hpid": ["HP:0000478"],
+  "index": [1651, 1668],
+  "matched": "eye abnormalities"},
+ {"hpid": ["HP:0000539"],
+  "index": [1670, 1692],
+  "matched": "refraction abnormality"},
+ {"hpid": ["HP:0001382", "HP:0002761"],
+  "index": [1752, 1771],
+  "matched": "joint hypermobility"},
+ {"hpid": ["HP:0001763"], "index": [1780, 1790], "matched": "pes planus"},
+ {"hpid": ["HP:0040083"], "index": [1799, 1810], "matched": "toe walking"},
+ {"hpid": ["HP:0000403"],
+  "index": [1870, 1892],
+  "matched": "recurrent otitis media"},
+ {"hpid": ["HP:0001609"], "index": [1926, 1938], "matched": "hoarse voice"},
+ {"hpid": ["HP:0004322"],
+  "index": [2458, 2475],
+  "matched": "Short stature was"},
+ {"hpid": ["HP:0004325"], "index": [2586, 2601], "matched": "low body weight"},
+ {"hpid": ["HP:0025502"], "index": [2633, 2648], "matched": "were overweight"},
+ {"hpid": ["HP:0000581"],
+  "index": [2930, 2951],
+  "matched": "were blepharophimosis"},
+ {"hpid": ["HP:0000426"],
+  "index": [2978, 3000],
+  "matched": "prominent nasal bridge"},
+ {"hpid": ["HP:0000455"], "index": [3008, 3023], "matched": "broad nasal tip"},
+ {"hpid": ["HP:0000582"],
+  "index": [3074, 3103],
+  "matched": "upslanting palpebral fissures"},
+ {"hpid": ["HP:0400000"], "index": [3123, 3132], "matched": "tall chin"},
+ {"hpid": ["HP:0000286"],
+  "index": [3140, 3156],
+  "matched": "epicanthal folds"},
+ {"hpid": ["HP:0000160"], "index": [3164, 3176], "matched": "narrow mouth"},
+ {"hpid": ["HP:0000218"], "index": [3184, 3195], "matched": "high palate"},
+ {"hpid": ["HP:0000358"],
+  "index": [3233, 3257],
+  "matched": "posteriorly rotated ears"},
+ {"hpid": ["HP:0000276"], "index": [3265, 3274], "matched": "long face"}]"""
+
+
+test_case11_text = """Collin et al. (2008) examined 6 affected individuals from 3 families with retinitis pigmentosa, 
+    all but 1 of whom displayed characteristic RP abnormalities including night blindness as the initial symptom, 
+    retinal bone-spicule pigmentation and attenuated retinal vessels, constriction of visual fields, 
+    and a nonrecordable ERG or ERG responses in a rod-cone pattern.
+    Two unrelated patients had posterior subcapsular cataracts. 
+    The authors observed differences in the photoreceptor dystrophy between the families: 
+    in 1 patient from family A, the cones were more severely affected than the rods (cone-rod pattern)
+    and kinetic visual fields were not constricted but showed bilateral central scotomas; 
+    fundus examination revealed central abnormalities at the level of the retinal pigment epithelium 
+    and moderate attenuation of retinal vessels. Her 60-year-old brother also had central fundus lesions,
+    but his ERG showed neither rod nor cone activity. Family B had relatively late onset of a classic form of RP, 
+    with preservation of central vision. The proband in family C, who was the youngest patient in the study, 
+    was the only one who was legally blind, due to severely constricted visual fields"
+"""
+
+test_case11_out = [{"hpid": ["HP:0032118"], "index": [74, 83], "matched": "retinitis"},
+                   {"hpid": ["HP:0000510"], "index": [74, 94], "matched": "retinitis pigmentosa"},
+                    {"hpid": ["HP:0000662"], "index": [166, 181], "matched": "night blindness"},
+{"hpid": ["HP:0000618"], "index": [172, 181], "matched": "blindness"},
+{"hpid": ["HP:0001133"], "index": [272, 301], "matched": "constriction of visual fields"},
+{"hpid": ["HP:0007787"], "index": [394, 425], "matched": "posterior subcapsular cataracts"},
+{"hpid": ["HP:0000523"], "index": [404, 425], "matched": "subcapsular cataracts"},
+{"hpid": ["HP:0000518"], "index": [416, 425], "matched": "cataracts"},
+{"hpid": ["HP:0000603"], "index": [680, 695], "matched": "central scotoma"},
+{"hpid": ["HP:0000575"], "index": [688, 695], "matched": "scotoma"},
+
+]
diff --git a/tests/test_extract.py b/tests/test_extract.py
index 368cffb..6f6ffd2 100644
--- a/tests/test_extract.py
+++ b/tests/test_extract.py
@@ -1,10 +1,19 @@
 import unittest
+import time
 import json
-
-from txt2hpo.extract import hpo, group_sequence
+from txt2hpo.extract import Extractor, Data, group_sequence
+from txt2hpo.data import load_model
+from tests.test_cases import *
+from txt2hpo.util import hpo_network
 
 
 class ExtractPhenotypesTestCase(unittest.TestCase):
+    def setUp(self):
+        self.startTime = time.time()
+
+    def tearDown(self):
+        t = time.time() - self.startTime
+        print('%s: %.3f' % (self.id(), t))
 
     def test_group_sequence(self):
         truth = [[0, 1], [3]]
@@ -12,66 +21,395 @@ def test_group_sequence(self):
 
     def test_hpo(self):
 
+        extract = Extractor(correct_spelling=False)
+
         # Test extracting single phenotype
-        truth = json.dumps([{"hpid": ["HP:0001290"], "index": [0, 9], "matched": "hypotonia"}])
-        self.assertEqual(hpo("hypotonia"), truth)
+        truth = [{"hpid": ["HP:0001290"], "index": [0, 9], "matched": "Hypotonia"}]
+        self.assertEqual(extract.hpo("Hypotonia").entries_sans_context, truth)
 
         # Test adding non phenotypic term
-        truth = json.dumps([{"hpid": ["HP:0001290"], "index": [5, 14], "matched": "hypotonia"}])
-        self.assertEqual(hpo("word hypotonia"), truth)
+        truth = [{"hpid": ["HP:0001290"], "index": [5, 14], "matched": "hypotonia"}]
+        self.assertEqual(extract.hpo("Word hypotonia").entries_sans_context, truth)
 
         # Test handling punctuation
-        truth = json.dumps([{"hpid": ["HP:0001290"], "index": [6, 15], "matched": "hypotonia"}])
-        self.assertEqual(hpo("word, hypotonia"), truth)
+        truth = [{"hpid": ["HP:0001290"], "index": [6, 15], "matched": "hypotonia"}]
+        self.assertEqual(extract.hpo("Word, hypotonia").entries_sans_context, truth)
 
         # Test extracting a multiword phenotype
-        truth = json.dumps([{"hpid": ["HP:0001263"], "index": [0, 19], "matched": "developmental delay"}])
-        self.assertEqual(hpo("developmental delay"), truth)
+        truth = [{"hpid": ["HP:0001263"],
+                             "index": [0, 19], "matched": "Developmental delay"}]
+        self.assertEqual(extract.hpo("Developmental delay").entries_sans_context, truth)
 
         # Test extracting a multiword phenotype with reversed word order
-        truth = json.dumps([{"hpid": ["HP:0001263"], "index": [0, 19], "matched": "delay developmental"}])
-        self.assertEqual(hpo("delay developmental"), truth)
+        truth = [{"hpid": ["HP:0001263"], "index": [0, 19],
+                             "matched": "Delay developmental"}]
+
+        self.assertEqual(extract.hpo("Delay developmental").entries_sans_context, truth)
 
         # Test extracting a phenotype with inflectional endings
-        truth = json.dumps([{"hpid": ["HP:0001290"], "index": [0, 9], "matched": "hypotonic"}])
-        self.assertEqual(hpo("hypotonic"), truth)
+        truth = [{"hpid": ["HP:0001290"], "index": [0, 9], "matched": "Hypotonic"}]
+        self.assertEqual(extract.hpo("Hypotonic").entries_sans_context, truth)
 
         # Test extracting a multiword phenotype with inflectional endings and reversed order
-        truth = json.dumps([{"hpid": ["HP:0001263"], "index": [0, 19], "matched": "delayed development"}])
-        self.assertEqual(hpo("delayed development"), truth)
+        truth = [{"hpid": ["HP:0001263"], "index": [0, 19], "matched": "Delayed development"}]
+        self.assertEqual(extract.hpo("Delayed development").entries_sans_context, truth)
 
-        # Test extracting multiple phenotypes
-        truth = json.dumps([{"hpid": ["HP:0001290"], "index": [0, 9], "matched": "hypotonia"},
-                 {"hpid": ["HP:0001263"], "index": [11, 30], "matched": "developmental delay"}])
-        self.assertEqual(hpo("hypotonia, developmental delay"), truth)
+        # Test extracting multiword phenotype following an unrelated phenotypic term
+        truth = [{"hpid": ["HP:0000365"], "index": [6, 18], "matched": "hearing loss"}]
+        self.assertEqual(extract.hpo("Delay hearing loss").entries_sans_context, truth)
+
+        # Test extracting multiword phenotype preceding an unrelated phenotypic term
+        truth = [{"hpid": ["HP:0000365"], "index": [0, 12], "matched": "Hearing loss"}]
+        self.assertEqual(extract.hpo("Hearing loss following").entries_sans_context, truth)
+
+        # Test extracting two multiword phenotype preceding interrupted by an unrelated phenotypic term
+        truth = [
+                            {"hpid": ["HP:0000365"], "index": [0, 12], "matched": "Hearing loss"},
+                            {"hpid": ["HP:0001263"], "index": [23, 42], "matched": "developmental delay"},
+                            ]
+        self.assertEqual(extract.hpo("Hearing loss following developmental delay").entries_sans_context, truth)
 
         # Test spellchecker
-        truth = json.dumps([{"hpid": ["HP:0001290"], "index": [0, 9], "matched": "hypotonic"}])
-        self.assertEqual(hpo("hyptonic", correct_spelling=True), truth)
+        extract = Extractor(correct_spelling=True)
+        truth = [{"hpid": ["HP:0001290"], "index": [0, 9], "matched": "Hypotonic"}]
+
+        self.assertEqual(extract.hpo("Hyptonic").entries_sans_context, truth)
 
         truth = []
-        self.assertEqual(hpo("hyptonic", correct_spelling=False), truth)
+        extract = Extractor(correct_spelling=False)
+        self.assertEqual(extract.hpo("Hyptonic").entries_sans_context, truth)
+
+        truth = [
+            {"hpid": ["HP:0002757"],"index": [12, 30], "matched": "multiple fractures"},
+            {"hpid": ["HP:0000938"], "index": [35, 45], "matched": "osteopenia"},
+        ]
+
+        self.assertEqual(truth, extract.hpo("Female with multiple fractures and osteopenia NA NA").entries_sans_context)
+
+        truth = [{"hpid": ["HP:0001156"], "index": [30, 43], "matched": "brachydactyly"}]
+
+        self.assertEqual(truth, extract.hpo("Female with fourth metacarpal brachydactyly").entries_sans_context)
+
+        extract = Extractor(correct_spelling=False, remove_overlapping=False)
+        truth = [
+            {"hpid": ["HP:0000964"], "index": [10, 16], "matched": "eczema"},
+            {"hpid": ["HP:0000988"], "index": [18, 27], "matched": "skin rash"},
+            {"hpid": ["HP:0000988"], "index": [23, 27], "matched": "rash"},
+            {"hpid": ["HP:0008070"], "index": [33, 44], "matched": "sparse hair"},
+            ]
+        resp = extract.hpo("Male with eczema, skin rash, and sparse hair").entries_sans_context
+        self.assertEqual(truth, resp)
+
+        # Test extracting an abbreviated phenotype
+        truth = [{"hpid": ["HP:0001370"], "index": [0, 2], "matched": "RA"}]
+        self.assertEqual(extract.hpo("RA").entries_sans_context, truth)
+
+        # Test extracting multiple phenotypes
+        truth = [{"hpid": ["HP:0001290"], "index": [0, 9], "matched": "Hypotonia"},
+                            {"hpid": ["HP:0001263"], "index": [11, 30], "matched": "developmental delay"
+                             }]
+        self.assertEqual(extract.hpo("Hypotonia, developmental delay").entries_sans_context, truth)
+
+        # Test term indexing given max length of extracted text
+        extract = Extractor(correct_spelling=False, max_length=20, chunk_by="max_length")
+        truth = [
+            {"hpid": ["HP:0001263"], "index": [0, 19], "matched": "Developmental delay"},
+            {"hpid": ["HP:0001290"], "index": [21, 30], "matched": "hypotonia"}
+        ]
+        self.assertEqual(extract.hpo("Developmental delay, hypotonia").entries_sans_context, truth)
 
+    def test_stop_word_phenos(self):
         # Test extracting multiple phenotypes with max_neighbors
-        truth = json.dumps([{"hpid": ["HP:0001263"], "index": [0, 23], "matched": "developmental and delay"}])
-        self.assertEqual(hpo("developmental and delay", max_neighbors=2), truth)
+        extract = Extractor(correct_spelling=True, max_neighbors=3)
+        truth = [{"hpid": ["HP:0001263"], "index": [0, 23], "matched": "developmental and delay"}]
+        self.assertEqual(extract.hpo("developmental and delay").entries_sans_context, truth)
+
+        extract = Extractor(correct_spelling=True, max_neighbors=1)
         truth = []
-        self.assertEqual(hpo("developmental and delay", max_neighbors=1), truth)
+        self.assertEqual(extract.hpo("developmental and delay").entries_sans_context, truth)
+
+        extract = Extractor(correct_spelling=False)
 
         # Test extracting single phenotype followed by multiword phenotype
-        truth = json.dumps([{"hpid": ["HP:0100710"], "index": [17, 26], "matched": "impulsive"},
-                 {"hpid": ["HP:0000750"], "index": [0, 12], "matched": "speech delay"}])
-        self.assertEqual(hpo("speech delay and impulsive"), truth)
+        truth = [
+                 {"hpid": ["HP:0000750"], "index": [0, 12], "matched": "Speech delay"},
+                 {"hpid": ["HP:0100710"], "index": [17, 26], "matched": "impulsive"}
+        ]
+        self.assertEqual(extract.hpo("Speech delay and impulsive").entries_sans_context, truth)
 
         # Test extracting multiword phenotype followed by single phenotype
-        truth = json.dumps([{"hpid": ["HP:0100710"], "index": [0, 9], "matched": "impulsive"},
-                 {"hpid": ["HP:0000750"], "index": [14, 26], "matched": "speech delay"}])
-        self.assertEqual(hpo("impulsive and speech delay"), truth)
+        truth = [
+                 {"hpid": ["HP:0100710"], "index": [0, 9], "matched": "Impulsive"},
+                 {"hpid": ["HP:0000750"], "index": [14, 26], "matched": "speech delay"}
+                 ]
+        self.assertEqual(extract.hpo("Impulsive and speech delay").entries_sans_context, truth)
 
-        # Test term indexing given max length of extracted text
+    def test_hpo_big_text_spellcheck_on(self):
+        # test parsing a page
+        extract = Extractor(max_neighbors=2, remove_overlapping=False)
+        self.assertEqual(extract.hpo(test_case11_text).n_entries, 12)
+
+    def test_hpo_big_text_spellcheck_off(self):
+        # test parsing a page
+        extract = Extractor(max_neighbors=2, correct_spelling=False, remove_overlapping=True)
+        self.assertEqual(extract.hpo(test_case11_text).n_entries, 7)
+
+    def test_hpo_big_text_spellcheck_off_max3(self):
+        # test parsing a page
+        extract = Extractor(max_neighbors=3, correct_spelling=False, remove_overlapping=True)
+        self.assertEqual(extract.hpo(test_case11_text).n_entries, 8)
+
+    def test_hpo_big_text_max_neighbors(self):
+        # test parsing a page
+        extract = Extractor(max_neighbors=1, correct_spelling=True, remove_overlapping=False)
+        hpo_max_2 = extract.hpo(test_case11_text).hpids
+        extract = Extractor(max_neighbors=3, correct_spelling=True, remove_overlapping=False)
+        hpo_max_3 = extract.hpo(test_case11_text).hpids
+
+        self.assertNotEqual(hpo_max_2, hpo_max_3)
+
+    def test_iteration_over_chunks(self):
+        # test performing multiple extractions in a row
+        sentences = ['Developmental delay', 'Hypotonia']
+        extract = Extractor(correct_spelling=False)
+        for sentence in sentences:
+            self.assertNotEqual(extract.hpo(sentence).n_entries, 0)
+        sentences = ['Hypotonia', 'Developmental delay']
+        for sentence in sentences:
+            self.assertNotEqual(extract.hpo(sentence).n_entries, 0)
+        extract = Extractor(correct_spelling=True)
+        sentences = ['Developmental delay', 'Hypotonia']
+        for sentence in sentences:
+            self.assertNotEqual(extract.hpo(sentence).n_entries, 0)
+        sentences = ['Hypotonia', 'Developmental delay']
+        for sentence in sentences:
+            self.assertNotEqual(extract.hpo(sentence).n_entries, 0)
+        sentences = ['Developmental delay', 'Hypotonia']
+        for sentence in sentences:
+            self.assertNotEqual(extract.hpo(sentence).n_entries, 0)
+        sentences = ['Hypotonia', 'Developmental delay']
+        for sentence in sentences:
+            self.assertNotEqual(extract.hpo(sentence).n_entries, 0)
+
+    def test_conflict_resolver(self):
+
+        model = load_model()
+
+        extracted = [{"hpid": ["HP:0000729", "HP:0001631"],
+                    "index": [50, 53],
+                    "matched": "ASD",
+                    "context": "the sample, 14,16 children were diagnosed with ASD, of whom 5689 had neurological and"}]
+        truth = [{"hpid": ["HP:0000729"],
+                  "index": [50, 53],
+                  "matched": "ASD"}]
+
+        data = Data(entries=extracted, model=model)
+        data.resolve_conflicts()
+        self.assertEqual(truth, data.entries_sans_context)
+
+        extracted = [{"hpid": ["HP:0000729", "HP:0001631"],
+                  "index": [44, 47],
+                  "matched": "ASD",
+                  "context": "secundum, all underwent surgical repair for ASD except for 1 individual \
+                  whose defect spontaneously closed"}]
+
+        truth = [{"hpid": ["HP:0001631"],
+                  "index": [44, 47],
+                  "matched": "ASD"}]
+
+        data = Data(entries=extracted, model=model)
+        data.resolve_conflicts()
+        self.assertEqual(truth, data.entries_sans_context)
+
+        # Test conflict resolution if terms have identical context similarity scores
+
+        extracted = [{"hpid": ["HP:0001631", "HP:0001631"],
+                      "index": [44, 47],
+                      "matched": "ASD",
+                      "context": "secundum, all underwent surgical repair for ASD except for 1 individual \
+                          whose defect spontaneously closed"}]
+
+        # Expected result remove only one term
+        truth = [{"hpid": ["HP:0001631"],
+                  "index": [44, 47],
+                  "matched": "ASD"}]
+
+        data = Data(entries=extracted, model=model)
+        data.resolve_conflicts()
+        self.assertEqual(truth, data.entries_sans_context)
+
+        # Test conflict resolution if terms have identical context similarity scores
+
+        extracted = [{"hpid": ["HP:0001631", "HP:0001631"],
+                      "index": [44, 47],
+                      "matched": "ASD",
+                      "context": "secundum, all underwent surgical repair for ASD except for 1 individual \
+                                  whose defect spontaneously closed"}]
+
+        # Expected result remove only one term
+        truth = [{"hpid": ["HP:0001631"],
+                  "index": [44, 47],
+                  "matched": "ASD"}]
+
+        data = Data(entries=extracted, model=model)
+        data.resolve_conflicts()
+        self.assertEqual(truth, data.entries_sans_context)
+
+        # Test conflict resolution if 2 of 3 terms have identical context similarity scores
+
+        extracted = [{"hpid": ["HP:0000729", "HP:0001631", "HP:0001631"],
+                      "index": [44, 47],
+                      "matched": "ASD",
+                      "context": "secundum, all underwent surgical repair for ASD except for 1 individual \
+                                          whose defect spontaneously closed"}]
+
+        # Expected result remove only one term
+        truth = [{"hpid": ["HP:0001631"],
+                  "index": [44, 47],
+                  "matched": "ASD"}]
+
+        data = Data(entries=extracted, model=model)
+        data.resolve_conflicts()
+        self.assertEqual(truth, data.entries_sans_context)
+
+    def test_custom_synonyms(self):
+        # test adding custom synonyms
+        custom_syn = {"HP:0001263": ['DD', 'GDD'], "HP:0000729": ['ASD', 'PDD']}
+
+        extract = Extractor(custom_synonyms=custom_syn)
+        truth = [{"hpid": ["HP:0001263"], "index": [0, 3], "matched": "GDD"},
+                            {"hpid": ["HP:0001263"], "index": [4, 6], "matched": "DD"}]
+        self.assertEqual(extract.hpo("GDD DD").entries_sans_context, truth)
+
+    def test_extract_ambiguous(self):
+        # test resolver works
+        extract = Extractor(resolve_conflicts=True)
+        truth = [{"hpid": ["HP:0001631"], "index": [44, 47], "matched": "ASD"}]
+        test1 = extract.hpo("secundum, all underwent surgical repair for ASD except for 1 individual whose defect spontaneously closed")
+        self.assertEqual(truth, test1.entries_sans_context)
+
+    def test_conflict_instantiate(self):
+        # Test that reinstantiation does not affect results
+        extract = Extractor(resolve_conflicts=True)
+        self.assertEqual(extract.resolve_conflicts, True)
+        res = extract.hpo("ASD")
+        self.assertEqual(len(res.entries[0]['hpid']), 1)
+
+        extract = Extractor(resolve_conflicts=False)
+        self.assertEqual(extract.resolve_conflicts, False)
+        res = extract.hpo("ASD")
+        self.assertEqual(len(res.entries[0]['hpid']), 2)
+
+        extract = Extractor(resolve_conflicts=True)
+        self.assertEqual(extract.resolve_conflicts, True)
+        res = extract.hpo("ASD")
+        self.assertEqual(len(res.entries[0]['hpid']), 1)
+
+    def test_extract_from_repeated_context(self):
+        extract = Extractor()
+        truth = [{"hpid": ["HP:0000154"], "index": [16, 26], "matched": "wide mouth"}]
+        resp = extract.hpo("Wide gait and a wide mouth")
+        self.assertEqual(truth, resp.entries_sans_context)
+
+    def test_extract_json_property(self):
+        extract = Extractor(max_neighbors=2)
+        truth = json.dumps([{"hpid": ["HP:0000154"],
+                             "index": [16, 26],
+                             "matched": "wide mouth"}])
+        resp = extract.hpo("Wide gait and a wide mouth")
+        self.assertEqual(truth, resp.json)
+
+    def test_extract_without_negated(self):
+
+        # negation should not apply if negation is part of matched string
+        extract = Extractor(remove_negated=True)
+        resp = extract.hpo("the patient presents with absent speech")
+        self.assertEqual(resp.hpids, ['HP:0001344'])
+
+        extract = Extractor()
+        resp = extract.hpo("developmental delay and a wide mouth")
+        resp.detect_negation()
+        self.assertEqual(set(['HP:0000154', 'HP:0001263']), set(resp.hpids))
+
+        resp = extract.hpo("developmental delay with no wide mouth")
+        resp.detect_negation()
+        resp.remove_negated()
+        self.assertEqual(['HP:0001263'], resp.hpids)
+
+        extract = Extractor(remove_negated=True)
+        resp = extract.hpo("developmental delay without a wide mouth")
+        self.assertEqual(['HP:0001263'], resp.hpids)
+
+        extract = Extractor(remove_negated=True)
+        resp = extract.hpo("no developmental delay, but has a wide mouth")
+        self.assertEqual(['HP:0000154'], resp.hpids)
+
+        extract = Extractor(remove_negated=True)
+        resp = extract.hpo("the patient has a wide mouth but no developmental delay.")
+        self.assertEqual(['HP:0000154'], resp.hpids)
+
+        extract = Extractor(remove_negated=True)
+        resp = extract.hpo("the patient does not have either a wide mouth or developmental delay.")
+        self.assertEqual([], resp.hpids)
+
+    def test_capitalization_affecting_outcome(self):
+        extract = Extractor(correct_spelling=False)
+        resp = extract.hpo("enlarged heart")
+        self.assertEqual(resp.hpids, ['HP:0001640'])
+
+        resp = extract.hpo(" enlarged heart")
+        self.assertEqual(resp.hpids, ['HP:0001640'])
+
+        resp = extract.hpo("Enlarged heart")
+        self.assertEqual(resp.hpids, ['HP:0001640'])
+
+        resp = extract.hpo(" Enlarged heart")
+        self.assertEqual(resp.hpids, ['HP:0001640'])
+
+        resp = extract.hpo("Male with Sotos, enlarged heart")
+        self.assertEqual(resp.hpids, ['HP:0001640'])
+        
+        resp = extract.hpo("Myoclonus Seizures")
+        self.assertEqual(set(resp.hpids), set(['HP:0002123']))
+
+    def test_remove_overlapping(self):
+        extract = Extractor(correct_spelling=False, remove_overlapping=False)
+        resp = extract.hpo("Polycystic kidney disease and myoclonus seizures.")
+        self.assertEqual(set(resp.hpids), set(['HP:0000113', 'HP:0000112', 'HP:0001250', 'HP:0002123','HP:0001336']))
+
+        extract = Extractor(correct_spelling=False, remove_overlapping=True)
+        resp = extract.hpo("Polycystic kidney disease and myoclonus seizures.")
+        self.assertEqual(set(resp.hpids), set(['HP:0002123','HP:0000113']))
+
+    def test_multiple_matches(self):
+        extract = Extractor(correct_spelling=False, remove_overlapping=True, resolve_conflicts=True)
+        resp = extract.hpo("l pre auricular ear pit.")
+        self.assertEqual(resp.hpids, ['HP:0004467'])
+        resp = extract.hpo("Coloboma, microphthalmia, macrocephaly, ear pit.")
+        self.assertEqual(set(resp.hpids), set(['HP:0000589', 'HP:0004467', 'HP:0000568', 'HP:0000256']))
+
+    def test_handing_term_hyphenation(self):
+        extract = Extractor(correct_spelling=False, remove_overlapping=True, resolve_conflicts=True)
+        hyphenated_phenos = \
+            [
+            (hpo_network.nodes()[x]['name'], x) for x in hpo_network.nodes() \
+            if (
+                    '-' in hpo_network.nodes()[x]['name'] and
+                    ',' not in hpo_network.nodes()[x]['name'] and
+                    '.' not in hpo_network.nodes()[x]['name']
+                )
+
+            ]
+        # Phenotypes where word-order is important is a limitation of current parsing method
+        known_bugs = ['HP:0000510', 'HP:0030932']
+        #known_bugs = []
+        long_phenos = ['HP:0011654', 'HP:0410303']
+        hyphenated_phenos = [x for x in hyphenated_phenos if x[1] not in known_bugs + long_phenos]
 
-        truth = json.dumps([
-                {"hpid": ["HP:0001263"], "index": [0, 19], "matched": "developmental delay"},
-                {"hpid": ["HP:0001290"], "index": [21, 30], "matched": "hypotonia"}
-                            ])
-        self.assertEqual(hpo("developmental delay, hypotonia", max_length=20), truth)
+        for test in hyphenated_phenos:
+            # current version is not expected to extract very long phenotypes
+            hpids = extract.hpo(test[0]).hpids
+            self.assertEqual(hpids, [test[1]])
+            # replace hyphens with space
+            hpids = extract.hpo(test[0].replace('-', ' ')).hpids
+            self.assertEqual(hpids, [test[1]])
diff --git a/tests/test_nlp.py b/tests/test_nlp.py
new file mode 100644
index 0000000..7ed8a3e
--- /dev/null
+++ b/tests/test_nlp.py
@@ -0,0 +1,36 @@
+import unittest
+import time
+
+from txt2hpo.nlp import similarity_term_to_context
+from txt2hpo.data import load_model
+
+
+class NLPCase(unittest.TestCase):
+    def setUp(self):
+        self.startTime = time.time()
+
+    def tearDown(self):
+        t = time.time() - self.startTime
+        print('%s: %.3f' % (self.id(), t))
+
+    def test_similarity_term_to_context(self):
+
+        model = load_model()
+
+        context1 = 'In the sample, 14,516 children were diagnosed with , of whom 5,689 had neurological symptoms'
+        context2 = 'secundum, all underwent surgical repair except for 1 individual whose defect spontaneously closed'
+
+        # "Autistic behavior"
+        term1 = 'HP:0000729'
+
+        # "Atrial septal defect"
+        term2 = 'HP:0001631'
+
+        t_aut_c_aut = similarity_term_to_context(term1, context1, model)
+        t_aut_c_chd = similarity_term_to_context(term1, context2, model)
+
+        t_chd_c_aut = similarity_term_to_context(term2, context1, model)
+        t_chd_c_chd = similarity_term_to_context(term2, context2, model)
+
+        self.assertGreater(t_aut_c_aut, t_chd_c_aut)
+        self.assertGreater(t_chd_c_chd, t_aut_c_chd)
\ No newline at end of file
diff --git a/tests/test_summarize.py b/tests/test_summarize.py
new file mode 100644
index 0000000..68b59e4
--- /dev/null
+++ b/tests/test_summarize.py
@@ -0,0 +1,60 @@
+import unittest
+
+import tests.test_cases as tc
+from txt2hpo.summarize import phenotype_distance, distances
+
+
+class ExtractPhenotypesTestCase(unittest.TestCase):
+
+    def test_phenotype_distance(self):
+        "[{'hpid': ['HP:0001290'], 'index': [895, 904], 'matched': 'hypotonia'}," \
+        "{'hpid': ['HP:0000218'], 'index': [3184, 3195], 'matched': 'high palate'}]"
+        truth = [('HP:0000218', 'HP:0001290', abs(3184 - 895) / 3184)]
+        result = phenotype_distance(tc.test_case0)
+        result = [x for x in result if x[0] != x[1]]
+        self.assertEqual(result, truth)
+
+        """[{"hpid": ["HP:0001290"], "index": [895, 904], "matched": "hypotonia"}, 
+         {"hpid": ["HP:0002014"], "index": [1597, 1605], "matched": "diarrhea"}, 
+         {"hpid": ["HP:0000218"], "index": [3184, 3195], "matched": "high palate"}]"""
+
+        truth = [('HP:0002014', 'HP:0001290', 0.22047738693467336),
+                 ('HP:0000218', 'HP:0001290', 0.7189070351758794),
+                 ('HP:0000218', 'HP:0002014', 0.498429648241206)]
+        result = phenotype_distance(tc.test_case1)
+        result = [x for x in result if x[0] != x[1]]
+        self.assertEqual(result, truth)
+
+        # Hypotonia is seen twice
+        """[{"hpid": ["HP:0001290"], "index": [895, 904], "matched": "hypotonia"},
+              {"hpid": ["HP:0001290"], "index": [1095, 1104], "matched": "hypotonia"},
+              {"hpid": ["HP:0002014"], "index": [1597, 1605], "matched": "diarrhea"}, 
+             {"hpid": ["HP:0000218"], "index": [3184, 3195], "matched": "high palate"}]"""
+
+        # Multiple unique entries for repeated term
+        truth = [('HP:0002014', 'HP:0001290', 0.22047738693467336),
+                 ('HP:0000218', 'HP:0001290', 0.7189070351758794),
+                 ('HP:0002014', 'HP:0001290', 0.15766331658291458),
+                 ('HP:0000218', 'HP:0001290', 0.6560929648241206),
+                 ('HP:0000218', 'HP:0002014', 0.498429648241206)]
+        result = phenotype_distance(tc.test_case2)
+        result = [x for x in result if x[0] != x[1]]
+        self.assertEqual(result, truth)
+
+    def test_distances(self):
+        test_cases = [tc.test_case0, tc.test_case1, tc.test_case2]
+        truth = [{'idx1': 'HP:0001290', 'idx2': 'HP:0001290', 'mean_score': 0.0, 'n': 3 },
+                 {'idx1': 'HP:0000218', 'idx2': 'HP:0001290', 'mean_score': 0.6979690117252931, 'n': 3},
+                 {'idx1': 'HP:0000218', 'idx2': 'HP:0000218', 'mean_score': 0.0, 'n': 3},
+                 {'idx1': 'HP:0001290', 'idx2': 'HP:0002014', 'mean_score': 0.18907035175879397, 'n': 2},
+                 {'idx1': 'HP:0002014', 'idx2': 'HP:0002014', 'mean_score': 0.0, 'n': 2},
+                 {'idx1': 'HP:0000218', 'idx2': 'HP:0002014', 'mean_score': 0.498429648241206, 'n': 2}]
+        result = distances(test_cases, min_n_distances=1).to_dict("records")
+        self.assertEqual(result, truth)
+
+        truth = [{'idx1': 'HP:0001290', 'idx2': 'HP:0001290', 'mean_score': 0.0, 'n': 3},
+                 {'idx1': 'HP:0000218', 'idx2': 'HP:0001290', 'mean_score': 0.6979690117252931, 'n': 3},
+                 {'idx1': 'HP:0000218', 'idx2': 'HP:0000218', 'mean_score': 0.0, 'n': 3}]
+
+        result = distances(test_cases, min_n_distances=2).to_dict("records")
+        self.assertEqual(result, truth)
\ No newline at end of file
diff --git a/txt2hpo/__init__.py b/txt2hpo/__init__.py
index fa31794..cd29372 100644
--- a/txt2hpo/__init__.py
+++ b/txt2hpo/__init__.py
@@ -1,2 +1,2 @@
 __project__ = 'txt2hpo'
-__version__ = '0.1.0'
\ No newline at end of file
+__version__ = '0.2.0'
\ No newline at end of file
diff --git a/txt2hpo/build_tree.py b/txt2hpo/build_tree.py
index d3cfbfc..47745e9 100644
--- a/txt2hpo/build_tree.py
+++ b/txt2hpo/build_tree.py
@@ -2,41 +2,48 @@
 import pickle
 import sys
 from txt2hpo.config import logger, config
-from txt2hpo.nlp import nlp
+from txt2hpo.util import hpo_network
+from txt2hpo.nlp import nlp_sans_ner
 from txt2hpo.nlp import st
-from phenopy.config import config as phenopy_config
 
-from phenopy import generate_annotated_hpo_network
 
-
-obo_file = phenopy_config.get('hpo', 'obo_file')
-
-disease_to_phenotype_file = phenopy_config.get('hpo', 'disease_to_phenotype_file')
-
-hpo_network, alt2prim, disease_records = \
-    generate_annotated_hpo_network(obo_file,
-                                   disease_to_phenotype_file,
-                                   annotations_file=None,
-                                   ages_distribution_file=None
-                                   )
-
-def build_search_tree():
+def build_search_tree(custom_synonyms=None, masked_terms=None):
     """
     Build stemmed, search tree for phenotypes / n-grams
     :param hpo: hpo object from phenopy
+    :param custom_synonyms: dictionary of hpo-id (key), list of synonyms (value)
+    :param masked_terms: block specific hpids from parsing
     :return: nested dictionary
     """
+    if custom_synonyms == None:
+        custom_synonyms = {}
+
+    if masked_terms == None:
+        masked_terms = ['HP:0000001']
+    else:
+        masked_terms += ['HP:0000001']
+
     terms = {}
-    print("")
     logger.info('Building a stemmed parse tree, this may take a few seconds, dont worry this is a one time thing \n')
+
+    for hpid, synonyms in custom_synonyms.items():
+        if hpid in masked_terms:
+            continue
+        if hpid in hpo_network.nodes():
+            if 'synonyms' in hpo_network.nodes[hpid]:
+                hpo_network.nodes[hpid]['synonyms'] += synonyms
+            else:
+                hpo_network.nodes[hpid]['synonyms'] = synonyms
+
     i = 0
     n_nodes = len(hpo_network.nodes)
 
     for node in hpo_network:
+        if node in masked_terms:
+            continue
         term = hpo_network.nodes[node]['name']
         if 'synonyms' in hpo_network.nodes[node]:
             synonyms = hpo_network.nodes[node]['synonyms']
-
         else:
             synonyms = []
 
@@ -45,19 +52,24 @@ def build_search_tree():
         # extend names using custom rules
         extended_names = []
         for name in names:
-
-            extended_names.append(name)
+            name = name.replace(', ',' ')
+            name = name.replace(',', ' ')
+            extended_names.append(name.lower())
+            extended_names.append(name.capitalize())
+            extended_names.append(name.title())
+            extended_names.append(name.replace('-',' '))
             extended_names.append(name.replace('Abnormality', 'Disorder'))
 
         for name in extended_names:
 
-            tokens = nlp(name)
+            tokens = nlp_sans_ner(name)
             tokens = [st.stem(st.stem(x.lemma_.lower())) for x in tokens if not x.is_stop and not x.is_punct]
             for token in tokens:
                 if token not in terms:
                     terms[token] = {}
                 if len(tokens) not in terms[token]:
                     terms[token][len(tokens)] = {}
+
                 name_identifier = ' '.join(sorted(tokens))
                 if name_identifier not in terms[token][len(tokens)]:
                     terms[token][len(tokens)][name_identifier] = [node]
@@ -101,3 +113,4 @@ def update_progress(progress):
     search_tree = build_search_tree()
     with open(config.get('tree', 'parsing_tree'), 'wb') as fh:
         pickle.dump(search_tree, fh)
+
diff --git a/txt2hpo/config.py b/txt2hpo/config.py
index 4610f2c..7af2103 100644
--- a/txt2hpo/config.py
+++ b/txt2hpo/config.py
@@ -1,6 +1,7 @@
 import configparser
 import logging
 import os
+from gensim.models import KeyedVectors
 from txt2hpo import __project__, __version__
 
 # create logger
@@ -49,8 +50,17 @@
             data_directory,
             'parsing_tree.pkl',
         ),
-
     }
+    config['models'] = {}
+    d2v_path = os.path.join(os.path.dirname(__file__), 'data/doc2vec_dm0_tagUniq_ep51_sa1e-05_vs40_ws18_mc5_neg5.wv.gz')
+    d2v_vw_path = os.path.join(data_directory, 'doc2vec.wv')
+    wv = KeyedVectors.load(d2v_path)
+    wv.save(d2v_vw_path)
+    config['models']['doc2vec'] = d2v_vw_path
+
+    config['data'] = {}
+    spellcheck_vocab_path = os.path.join(os.path.dirname(__file__), 'data/spellcheck_vocab_upd032020.json')
+    config['data']['spellcheck_vocab'] = spellcheck_vocab_path
 
     with open(os.path.join(config_directory, 'txt2hpo.ini'), 'w') as configfile:
         logger.info('writing config file to: %s '%config_directory)
@@ -65,4 +75,3 @@
 
     config.read(config_file)
     logger.info(f'Using configuration file: {config_file}')
-
diff --git a/txt2hpo/data/__init__.py b/txt2hpo/data/__init__.py
index e69de29..56c8109 100644
--- a/txt2hpo/data/__init__.py
+++ b/txt2hpo/data/__init__.py
@@ -0,0 +1,16 @@
+import json
+from gensim.models import KeyedVectors
+from txt2hpo.config import config
+
+
+def load_model():
+    if 'doc2vec' in config['models']:
+        wv = KeyedVectors.load(config['models']['doc2vec'])
+    return wv
+
+
+def load_spellcheck_vocab():
+    if 'spellcheck_vocab' in config['data']:
+        with open(config['data']['spellcheck_vocab'], "rt") as fh:
+            spellcheck_vocab = json.load(fh)
+    return spellcheck_vocab
diff --git a/txt2hpo/data/doc2vec_dm0_tagUniq_ep51_sa1e-05_vs40_ws18_mc5_neg5.wv.gz b/txt2hpo/data/doc2vec_dm0_tagUniq_ep51_sa1e-05_vs40_ws18_mc5_neg5.wv.gz
new file mode 100644
index 0000000..42cc203
Binary files /dev/null and b/txt2hpo/data/doc2vec_dm0_tagUniq_ep51_sa1e-05_vs40_ws18_mc5_neg5.wv.gz differ
diff --git a/txt2hpo/data/spellcheck_vocab.py b/txt2hpo/data/spellcheck_vocab.py
deleted file mode 100644
index d32cd8f..0000000
--- a/txt2hpo/data/spellcheck_vocab.py
+++ /dev/null
@@ -1 +0,0 @@
-spellcheck_vocab = {"dsbs": 3123, "be": 2822982, "repair": 15876, "by": 602024, "two": 122326, "general": 11096, "process": 47727, "the": 4051289, "\ufeff1": 46661, "use": 317076, "a": 1868733, "intact": 5200, "homologous": 4158, "sequence": 90683, "and": 2573484, "refer": 6476, "to": 1457757, "recombination": 4362, "hr": 16812, "2": 187502, "join": 1305, "dna": 109683, "double": 12590, "strand": 12332, "end": 18939, "dse": 218, "in": 2485014, "homology": 4823, "independent": 34536, "manner": 14821, "1": 216871, "mammalian": 8251, "cell": 921620, "ej": 224, "of": 3064538, "dses": 47, "prominent": 4381, "dsb": 3014, "pathway": 81903, "canonical": 6138, "non": 63475, "c": 177328, "nhej": 2073, "which": 216127, "ku": 779, "ligase": 6627, "4": 106001, "dependent": 45055, "able": 15804, "conservative": 994, "way": 7775, "at": 323006, "junction": 8973, "although": 53389, "it": 146958, "adaptable": 24, "imperfectly": 21, "cohesive": 108, "2\u20134": 1196, "more": 5459, "recently": 26199, "alternative": 10873, "have": 464039, "describe": 83940, "that": 835897, "doe": 19703, "not": 295350, "require": 41585, "but": 122170, "initiate": 8215, "ctip": 1659, "single": 34896, "resection": 3761, "therefore": 51757, "highly": 27263, "mutagenic": 356, "typically": 4340, "generate": 33689, "deletion": 25611, "result": 192752, "from": 343757, "initial": 12209, "frequently": 8834, "microhomologies": 62, "distant": 3818, "resect": 1099, "2\u201310": 125, "several": 48342, "mechanism": 58857, "damage": 24164, "agent": 10667, "such": 76881, "ionize": 905, "radiation": 4786, "or": 362165, "reactive": 4678, "oxygen": 5289, "species": 12363, "nucleases": 296, "with": 1047683, "proximal": 5996, "prolonged": 188, "replication": 19969, "stress": 24262, "also": 198050, "11": 34647, "importantly": 10336, "arrest": 9043, "fork": 2790, "12,13": 1004, "structure": 45201, "inevitably": 179, "rearrangement": 4302, "we": 418360, "report": 115800, "cohesin": 1562, "complex": 72178, "prevent": 15615, "close": 11797, "specifically": 15447, "s": 24691, "phase": 21643, "14": 36871, "alternatively": 4366, "can": 166897, "copy": 13670, "through": 56661, "template": 5838, "switch": 5584, "indeed": 17161, "model": 81852, "genetic": 53067, "account": 13088, "for": 719604, "numb": 59289, "variation": 17563, "upon": 26911, "microhomology": 203, "anneal": 3023, "propose": 14120, "mmbir": 53, "fostes": 51, "mediate": 56877, "break": 4143, "induce": 112380, "stalling": 7, "switching": 37, "15\u201317": 491, "yeast": 8954, "chromosome": 21385, "occur": 30848, "via": 28860, "between": 175728, "diverge": 439, "repeat": 20419, "18": 30377, "on": 317329, "one": 87518, "component": 23449, "tether": 1797, "allow": 18864, "their": 72552, "immediate": 1328, "ligation": 2543, "19\u201322": 196, "remarkably": 3174, "defective": 5347, "exhibit": 24728, "strong": 26997, "instability": 3882, "underline": 1353, "fact": 14467, "essential": 16474, "maintenance": 6619, "genome": 27275, "stability": 12041, "consistently": 6278, "protect": 6023, "against": 27583, "mobility": 3182, "thus": 58440, "unscheduled": 86, "23\u201325": 368, "conversely": 4936, "involve": 59038, "translocation": 9589, "mouse": 119086, "drosophila": 4064, "26\u201328": 355, "however": 108211, "lead": 58787, "appear": 24443, "much": 152425, "because": 38870, "show": 293789, "event": 19877, "capture": 5624, "excise": 1529, "chromosomal": 4952, "2,29": 9, "moreover": 26535, "analysis": 170355, "reveal": 47499, "occurrence": 4758, "addition": 57334, "direct": 27473, "this": 487758, "associate": 135304, "while": 53493, "control": 176119, "per": 21048, "se": 3047, "extensively": 3251, "study": 295570, "rearrange": 598, "poorly": 4748, "document": 4889, "here": 20219, "address": 7281, "question": 6153, "impact": 14703, "distance": 6353, "both": 135952, "efficiency": 9528, "accuracy": 2688, "analyze": 46150, "precise": 3605, "molecular": 35684, "level": 235295, "live": 27707, "human": 126160, "context": 10205, "intrachromosomal": 80, "substrate": 22213, "monitor": 8328, "target": 79923, "into": 76684, "meganuclease": 7, "i": 47725, "scei.": 15, "these": 74339, "derive": 27764, "previously": 84890, "characterize": 21077, "validate": 10616, "discuss": 2577, "only": 86646, "few": 12274, "kb": 10708, "short": 17334, "nucleus": 20615, "scale": 8386, "sufficient": 9756, "significantly": 94106, "reduce": 70167, "error": 6586, "prone": 2006, "complexly": 10, "particularly": 9845, "favor": 3011, "partial": 5988, "duplication": 4720, "reporter": 15429, "ectopic": 4502, "ecs": 2842, "promote": 35998, "counteract": 1546, "53bp1": 2035, "suggest": 125676, "involvement": 9697, "initiation": 7163, "accord": 30691, "datum": 128088, "pattern": 26724, "speculate": 5387, "micro": 1483, "arise": 4897, "thereby": 8976, "indicate": 95569, "requirement": 4115, "spatio": 100, "temporal": 2814, "gap": 3830, "couple": 8318, "insertion": 5574, "possibility": 13810, "alter": 23167, "even": 20788, "add": 18488, "additional": 37821, "risk": 71638, "integrity": 4201, "design": 13049, "key": 17963, "difference": 62227, "scei": 450, "site": 84243, "34": 15685, "bp": 14839, "versus": 15230, "3200": 33, "fig": 291203, "1a": 22942, "s1": 23456, "supplementary": 65192, "information": 16803, "34-bp": 7, "should": 14230, "rapid": 6731, "contrast": 37105, "3200-bp": 6, "absolutely": 403, "synapsis": 1217, "step": 15109, "bring": 1713, "together": 26655, "prior": 13141, "expression": 352343, "gfp": 26652, "cd4": 16199, "1b": 18793, "a.": 3235, "comparable": 8286, "backbone": 2211, "construct": 27407, "hybrid": 3125, "34bp": 3, "interchange": 93, "internal": 5710, "flank": 4782, "red": 12081, "arrow": 4277, "after": 111576, "cleavage": 12706, "see": 45203, "detail": 14412, "b.": 3144, "example": 16461, "fluorescence": 15701, "microscopy": 8875, "upper": 6672, "panel": 23547, "flow": 13011, "cytometry": 6578, "low": 97782, "give": 18859, "line": 92969, "establish": 20888, "clone": 24683, "bear": 5915, "sv40-transformed": 53, "fibroblast": 18486, "note": 19479, "type": 107394, "frequency": 42252, "do": 82754, "vary": 9285, "different": 81728, "same": 41907, "absence": 26232, "position": 20076, "effect": 143852, "3.5-fold": 154, "high": 159113, "contain": 69291, "than": 108821, "kilobases": 160, "separation": 2573, "restrict": 6928, "large": 38147, "genomic": 21149, "33": 16542, "previous": 49500, "define": 23673, "class": 12756, "little": 56416, "3": 138948, "protrude": 333, "nucleotide": 15950, "eventually": 2211, "delete": 3979, "all": 135178, "four": 29946, "again": 5886, "separate": 12050, "produce": 23363, "proportion": 10564, "rejoin": 113, "64": 3735, "57": 5120, "hifi": 42, "fidelity": 765, "i.e.": 13292, "free": 20452, "7": 51193, "gck20": 2, "each": 75344, "gc92": 4, "gc49": 4, "40": 23074, "36": 15728, "23": 23717, "10": 95529, "vs.": 17055, "0.007": 1181, "p": 167365, "0.005": 2196, "t": 103679, "test": 88740, "table": 115899, "s2": 14696, "foster": 2488, "likely": 28224, "antagonize": 1526, "reproduce": 1077, "silence": 15906, "3a": 19059, "increase": 185486, "silencing": 681, "37": 25918, "percentage": 14094, "size": 30284, "3b": 15223, "impair": 14545, "56": 5588, "52": 6550, "53bp1-depleted": 11, "affect": 59034, "depletion": 16586, "necessary": 9563, "extensive": 4960, "degradation": 22595, "compensate": 1702, "avoid": 3463, "attack": 1202, "generation": 10511, "long": 24042, "seem": 11006, "\u2265": 3521, "45": 12847, "200": 8670, "compare": 111337, "4a": 17169, "4b": 13607, "strikingly": 2437, "2.5-fold": 386, "tables": 4131, "s3": 10233, "interestingly": 24523, "decrease": 70963, "abolish": 6031, "stimulation": 21365, "no": 103998, "collectively": 4702, "deprotection": 36, "efficiently": 5242, "matter": 75, "sequencing": 2118, "insertional": 103, "classify": 7724, "main": 10219, "category": 4578, "s4": 7301, "entail": 262, "adjacent": 7876, "either": 42784, "5": 104693, "31": 17434, "over": 29757, "63": 3848, "\u226545": 30, "total": 48627, 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\ No newline at end of file
diff --git a/txt2hpo/data/spellcheck_vocab_upd032020.json b/txt2hpo/data/spellcheck_vocab_upd032020.json
new file mode 100644
index 0000000..f25dcaf
--- /dev/null
+++ b/txt2hpo/data/spellcheck_vocab_upd032020.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/txt2hpo/extract.py b/txt2hpo/extract.py
index ad35630..6ce8218 100644
--- a/txt2hpo/extract.py
+++ b/txt2hpo/extract.py
@@ -1,151 +1,451 @@
-
 import json
+import numpy as np
+from itertools import combinations, chain
+import spacy
+import re
+
 from txt2hpo.build_tree import update_progress, hpo_network
 from txt2hpo.config import logger
 from txt2hpo.spellcheck import spellcheck
-from txt2hpo.nlp import nlp
+from txt2hpo.nlp import nlp_model, nlp_sans_ner, similarity_term_to_context
 from txt2hpo.nlp import st
-from txt2hpo.build_tree import search_tree
+from txt2hpo.data import load_model
+from txt2hpo.build_tree import search_tree, build_search_tree
+from txt2hpo.util import remove_key
 
 
-def group_sequence(lst):
-    """
-    Break a sequence of integers into continuous groups
-    [1,2,3,5,7,8] -> [[1,2,3],[5],[7,8]]
-    :param lst: list of ints
-    :return: list of lists
+class Data(object):
+    def __init__(self, entries=None, model=None, negation_model=None):
+        if not entries:
+            self.entries = []
+        else:
+            self.entries = entries
+        self.model = model
+        self.negation_model = negation_model
+
+    def add(self,entry):
+        self.entries += entry
+
+    def remove(self, item):
+        self.entries.remove(item)
+
+    def remove_tagged(self, tag, state=True):
+        to_remove = [entry for entry in self.entries if entry[tag] is state]
+        for element in to_remove:
+            self.remove(element)
+
+    def detect_negation(self):
+        for entry in self.entries:
+            entry['negated_tokens'] = ' '.join([e.text for e in self.negation_model(entry['context']).ents if e._.negex])
+            entry['negated'] = [t.text for t in nlp_sans_ner(entry['negated_tokens'])]
+            if isinstance(entry['matched_tokens'], (spacy.tokens.doc.Doc, spacy.tokens.span.Span)):
+                entry['matched_words'] = [t.text for t in entry['matched_tokens']]
+            elif isinstance(entry['matched_tokens'], spacy.tokens.token.Token):
+                entry['matched_words'] = [entry['matched_tokens'].text]
+            else:
+                entry['matched_words'] = []
+            entry['is_negated'] = True if set(entry['negated']).intersection(set(entry['matched_words'])) else False
+
+    def remove_negated(self):
+        self.detect_negation()
+        self.remove_tagged('is_negated')
+
+    def remove_overlapping(self):
+        self._mark_overlapping()
+        self.remove_tagged('is_longest', False)
+
+    def _mark_overlapping(self):
+        """
+        Keep only term with widest span from a list of entries
+        :return: Modify self.entries
+        """
+        range_dict = {}
+        for i, rec in enumerate(self.entries.copy()):
+            new_set = set(range(rec['index'][0], rec['index'][1]))
+            overlapping = [x for x in new_set if x in range_dict]
+            if overlapping:
+                prev_size = range_dict[overlapping[0]][0]
+                if len(new_set) > prev_size:
+                    for idx in new_set:
+                        range_dict[idx] = (len(new_set), i)
+            else:
+                for idx in new_set:
+                    range_dict[idx] = (len(new_set), i)
+
+        unique_elements = set([x[1] for x in range_dict.values()])
+
+        for i, rec in enumerate(self.entries.copy()):
+            if i in unique_elements:
+                rec['is_longest'] = True
+            else:
+                rec['is_longest'] = False
+            self.entries[i] = rec
+
+    def resolve_conflicts(self):
+        """
+        Pick most likely HPO ID based on context
+        :param extracted_terms: list of dictionaries identified by extract_hpo_terms
+        :return: list of dictionaries with resolved conflicts
+        """
+        if not self.model:
+            logger.critical("Doc2vec model does not exist or could not be loaded")
+
+        resolved_terms = []
+        for entry in self.entries:
+            similarity_scores = []
+            if len(entry['hpid']) > 1:
+                for term in entry['hpid']:
+                    similarity_scores.append(similarity_term_to_context(term, entry['context'], self.model))
+
+                # reduce matches until only one term left
+                for i in range(len(similarity_scores) - 1):
+                    idx_least_likely_term = similarity_scores.index(min(similarity_scores))
+                    least_likely_term = entry['hpid'][idx_least_likely_term]
+                    entry['hpid'].remove(least_likely_term)
+
+            resolved_terms.append(entry)
+        self.entries = resolved_terms
+
+    @property
+    def hpids(self):
+        return list(set(np.array([x['hpid'] for x in self.entries]).flatten()))
+
+    @property
+    def json(self):
+        result = self.entries_sans_context.copy()
+        return json.dumps(result)
+
+    @property
+    def contents(self):
+        return self.entries
+
+    @property
+    def entries_sans_context(self):
+        result = self.entries.copy()
+        result = sorted(result, key=lambda i: i['index'][0], reverse=False)
+        result = remove_key(result, 'context')
+        result = remove_key(result, 'matched_tokens')
+        result = remove_key(result, 'is_longest')
+        return result
+
+    @property
+    def n_entries(self):
+        return len(self.entries)
+
+
+class Extractor:
+
+    """ Converts text to HPO annotated JSON object
+
+    Args:
+        correct_spelling: (True,False) attempt to correct spelling using spellcheck
+        max_neighbors: (int) max number of phenotypic groups to attempt to search for a matching phenotype
+        max_length: (int) max document length in characters, higher limit will require more memory
+        context_window: (int) dimensions of context to return number of tokens in each direction
+        resolve_conflicts: (True,False) loads big model
+        custom_synonyms: (dict) dictionary of additional synonyms to map
+
     """
-    if not lst:
-        return []
-    grouped = [[lst[0]]]
-    for i in range(1, len(lst)):
-        if lst[i - 1] + 1 == lst[i]:
-            grouped[-1].append(lst[i])
+
+    def __init__(self, correct_spelling=True,
+                 resolve_conflicts=True,
+                 remove_negated=False,
+                 remove_overlapping=True,
+                 max_neighbors=3,
+                 max_length=1000000,
+                 context_window=8,
+                 model=None,
+                 custom_synonyms=None,
+                 negation_language="en",
+                 chunk_by='phrase'
+                 ):
+
+        self.correct_spelling = correct_spelling
+        self.resolve_conflicts = resolve_conflicts
+        self.remove_negated = remove_negated
+        self.remove_overlapping = remove_overlapping
+        self.max_neighbors = max_neighbors
+        self.max_length = max_length
+        self.context_window = context_window
+        self.negation_model = nlp_model(negation_language=negation_language)
+        self.chunk_by = chunk_by
+        if custom_synonyms:
+            self.search_tree = build_search_tree(custom_synonyms=custom_synonyms)
         else:
-            grouped.append([lst[i]])
-    return grouped
+            self.search_tree = search_tree
+        if model is None:
+            self.model = load_model()
 
-def index_tokens(stemmed_tokens):
-    """index phenotype tokens by matching each stem against root of search tree"""
-    phenotokens = []
-    phenindeces = []
+    def hpo(self, text):
+        """
+        extracts hpo terms from text
+        :param text: text of type string
+        :return: Data object
+        """
 
-    for i, token in enumerate(stemmed_tokens):
-        if token in search_tree:
-            phenotokens.append(token)
-            phenindeces.append(i)
+        nlp_sans_ner.max_length = self.max_length
 
-    return phenotokens, phenindeces
+        extracted_terms = Data(model=self.model, negation_model=self.negation_model)
 
+        len_last_chunk = 0
 
-def assemble_groups(groups, phen_groups, max_neighbors):
-    """Assemble adjacent groups of phenotypes into groups of various sizes"""
-    for i in range(len(groups)):
-        if groups[i] not in phen_groups:
-            phen_groups.append(groups[i])
+        if self.chunk_by == "max_length":
 
-        # make sure not to modify original groups object
-        adjacent_groups = groups[i].copy()
-        for j in range(1, max_neighbors):
-            if len(groups) > i + j:
-                adjacent_groups += groups[i + j]
-                if adjacent_groups not in phen_groups:
-                    phen_groups.append(adjacent_groups)
-    return phen_groups
+            chunks = [text[i:i + self.max_length] for i in range(0, len(text), self.max_length)]
+        elif self.chunk_by == "phrase":
+            chunks = re.split(";|,|\n|\r|\.", text)
 
+        for i, chunk in enumerate(chunks):
 
-def find_hpo_terms(phen_groups, stemmed_tokens, tokens, base_index):
-    """Match hpo terms from stemmed tree to indexed groups in text"""
-    extracted_terms = []
-    for phen_group in phen_groups:
-        # if there is only one phenotype in a group
-        if len(phen_group) == 1:
-            grp_phen_tokens = stemmed_tokens[phen_group[0]]
+            if self.correct_spelling:
+                chunk = spellcheck(chunk)
 
-        # if multiple phenotypes, get all words between
-        else:
-            grp_phen_tokens = " ".join(stemmed_tokens[min(phen_group):max(phen_group)+1])
+            tokens = nlp_sans_ner(chunk)
 
-        # remove stop words and punctuation from group of phenotypes
-        grp_phen_tokens = nlp(grp_phen_tokens)
-        grp_phen_tokens = [x.text for x in grp_phen_tokens if not x.is_stop and not x.is_punct]
+            # Stem tokens
+            stemmed_tokens = [st.stem(st.stem(x.lemma_.lower())) for x in tokens]
+
+            # Index tokens which match stemmed phenotypes
+            phenotokens, phenindeces = self.index_tokens(stemmed_tokens)
+
+            # Group token indices
+            groups = group_sequence(phenindeces)
+
+            # Add leave one out groups
+            groups = permute_leave_one_out(groups)
+
+            # Find and fuse adjacent phenotype groups
+            assembled_groups = assemble_groups(groups, max_distance=self.max_neighbors)
+
+            phen_groups = recombine_groups(assembled_groups)
 
-        # sort to match same order as used in making keys for search tree
-        try_term_key = ' '.join(sorted(grp_phen_tokens))
+            # Extract hpo terms keep track of chunked coordinates, split character len=1
 
-        # attempt to extract hpo terms from tree based on root, length of phrase and key
-        try:
-            hpids = search_tree[grp_phen_tokens[0]][len(grp_phen_tokens)][try_term_key]
+            extracted_terms.add(self.find_hpo_terms(tuple(phen_groups),
+                                              tuple(stemmed_tokens),
+                                              tokens,
+                                              base_index=len_last_chunk,
+                                              ))
+            if self.chunk_by == 'phrase':
+                len_last_chunk += len(chunk) + 1
+            elif self.chunk_by == 'max_length':
+                len_last_chunk += len(chunk)
 
-        except (KeyError, IndexError):
-            hpids = []
+        if extracted_terms:
+            if self.resolve_conflicts is True:
+                extracted_terms.resolve_conflicts()
+            else:
+                pass
+
+        if self.remove_negated:
+            extracted_terms.remove_negated()
+
+        if self.remove_overlapping:
+            extracted_terms.remove_overlapping()
+
+        return extracted_terms
+
+    def find_hpo_terms(self, phen_groups, stemmed_tokens, tokens, base_index):
+        """Match hpo terms from stemmed tree to indexed groups in text"""
+        extracted_terms = []
 
-        # if found any hpids, append to extracted
-        if hpids:
+        # remove stop words and punctuation from group of phenotypes
+        stop_punct_mask = [x.i for x in tokens if x.is_stop or x.is_punct]
+        cln_phen_groups = []
+        for grp in phen_groups:
+            cand_grp = [x for x in grp if not x in stop_punct_mask]
+            if cand_grp:
+                cln_phen_groups.append(cand_grp)
+
+        for phen_group in cln_phen_groups:
+
+            # if there is only one phenotype in a group
             if len(phen_group) == 1:
-                matched_string = tokens[phen_group[0]]
-                start = tokens[phen_group[0]].idx
-                end = start + len(tokens[phen_group[0]])
+                phen_group_string = stemmed_tokens[phen_group[0]]
+                phen_group_strings = [phen_group_string]
+
+            # if multiple phenotypes, get all words between
             else:
-                matched_string = tokens[min(phen_group):max(phen_group)+1]
-                start = tokens[phen_group[0]:phen_group[-1]+1].start_char
-                end = tokens[phen_group[0]:phen_group[-1]+1].end_char
+                phen_start = min(phen_group)
+                phen_stop = max(phen_group) + 1
+                phen_group_tokens = tokens[phen_start:phen_stop]
+                phen_group_tokens_minus_trash = [x for x in phen_group_tokens if not x.is_stop and not x.is_punct]
+                phen_group_tokens_minus_trash_idx = [x.i for x in phen_group_tokens_minus_trash]
+                phen_group_strings = [stemmed_tokens[x] for x in phen_group_tokens_minus_trash_idx] #phen_group_tokens_minus_trash_idx
 
-            extracted_terms.append({"hpid":hpids, "index":[base_index + start, base_index + end], "matched":matched_string.text})
-    return extracted_terms
+            try_term_key = ' '.join(sorted(phen_group_strings))
 
+            # attempt to extract hpo terms from tree based on root, length of phrase and key
+            try:
+                # copy matching hpids, because we may need to delete conflicting terms without affecting this obj
+                hpids = self.search_tree[phen_group_strings[0]][len(phen_group_strings)][try_term_key].copy()
 
-def hpo(text, correct_spelling=True, max_neighbors=5, max_length=1000000):
-    """
-    extracts hpo terms from text
-    :param text: text of type string
-    :param correct_spelling: (True,False) attempt to correct spelling using spellcheck
-    :param max_neighbors: (int) max number of phenotypic groups to attempt to search for a matching phenotype
-    :param max_length: (int) max document length in characters, higher limit will require more memory
-    :return: json of hpo terms, their indices in text and matched string
-    """
+            except (KeyError, IndexError):
+                hpids = []
+
+            # if found any hpids, append to extracted
+            if hpids:
+                # extract span of just matching phenotype tokens
+                matching_tokens_index = [x.i for x in tokens if x.i in phen_group]
+
+                if len(matching_tokens_index) == 1:
+                    matched_tokens = tokens[matching_tokens_index[0]]
+                    matched_string = matched_tokens.text
+                    start = matched_tokens.idx
+                    end = start + len(matched_string)
+
+                else:
+                    matched_tokens = tokens[min(matching_tokens_index):max(matching_tokens_index) + 1]
+                    matched_string = matched_tokens.text
+                    start = matched_tokens.start_char
+                    end = matched_tokens.end_char
+
+                if min(matching_tokens_index) < self.context_window:
+                    context_start = 0
+                else:
+                    context_start = min(matching_tokens_index) - self.context_window
 
-    nlp.max_length = max_length
+                if max(matching_tokens_index) + self.context_window >= len(tokens) - 1:
+                    context_end = len(tokens)
 
-    extracted_terms = []
+                else:
+                    context_end = max(matching_tokens_index) + self.context_window
 
-    chunks = [text[i:i + max_length] for i in range(0, len(text), max_length)]
-    len_last_chunk = 1
-    for i, chunk in enumerate(chunks):
-        if correct_spelling:
-            chunk = spellcheck(chunk)
-            if len(chunk) < max_length:
-                nlp.max_length = len(chunk)
+                if context_start == context_end:
+                    context = tokens[context_start]
 
-        tokens = nlp(chunk)
+                else:
+                    context = tokens[context_start:context_end]
 
-        # Stem tokens
-        stemmed_tokens = [st.stem(st.stem(x.lemma_.lower())) for x in tokens]
+                found_term = dict(hpid=hpids,
+                                  index=[base_index + start, base_index + end],
+                                  matched=matched_string,
+                                  context=context.text,
+                                  matched_tokens=matched_tokens,
+                                  )
 
-        # Index tokens which match stemmed phenotypes
-        phenotokens, phenindeces = index_tokens(stemmed_tokens)
+                if found_term not in extracted_terms:
+                    extracted_terms.append(found_term)
 
-        # Group token indices
-        groups = group_sequence(phenindeces)
-        phen_groups = []
+        return extracted_terms
 
-        # Add individual word token indices to phenotype groups
-        phen_groups += [[x] for x in phenindeces]
+    def index_tokens(self, stemmed_tokens):
+        """index phenotype tokens by matching each stem against root of search tree"""
+        phenotokens = []
+        phenindices = []
+        for i, token in enumerate(stemmed_tokens):
+            if token in self.search_tree:
+                phenotokens.append(token)
+                phenindices.append(i)
 
-        # Find and fuse adjacent phenotype groups
-        phen_groups = assemble_groups(groups, phen_groups, max_neighbors)
+        return phenotokens, phenindices
 
-        # Extract hpo terms
-        extracted_terms += find_hpo_terms(phen_groups, stemmed_tokens, tokens, base_index=i * len_last_chunk)
-        len_last_chunk = len(chunk)
 
-    if extracted_terms:
-        return json.dumps(extracted_terms)
-    else:
+def group_sequence(lst):
+    """
+    Break a sequence of integers into continuous groups
+    [1,2,3,5,7,8] -> [[1,2,3],[5],[7,8]]
+    :param lst: list of ints
+    :return: list of lists
+    """
+    if not lst:
         return []
+    grouped = [[lst[0]]]
+    for i in range(1, len(lst)):
+        if lst[i - 1] + 1 == lst[i]:
+            grouped[-1].append(lst[i])
+        else:
+            grouped.append([lst[i]])
+    return grouped
 
 
-def self_evaluation(correct_spelling=False):
+def assemble_groups(original, max_distance=2, min_compl=0.20):
+    """
+    Join adjacent groups of phenotypes into new groups
+    [[1,2],[5,6]] -> {((1, 2), (5, 6)), (1, 2), (5, 6)}
+    :param original: Original list of lists of integers
+    :param max_distance: Maximum number of combinations
+    :param min_compl: Minimum fraction of complete term
+    :return: set of grouped tuple index sequences
+    """
+
+    def missing_elements(L):
+        start, end = L[0], L[-1]
+        return sorted(set(range(start, end + 1)).difference(L))
+
+    ori_set = set(tuple(set(x)) for x in original)
+
+    fused_set = set()
+    final_set = set()
+    fused_set_uniq = set()
+
+    for distance in range(1, max_distance):
+        combs = list(combinations(ori_set, distance))
+        for comb in combs:
+            fused_comb = tuple(set(np.concatenate(comb)))
+            if fused_comb in ori_set:
+                continue
+            else:
+                fused_set.add(tuple(set(comb)))
+
+        for item in fused_set:
+            if len(set(np.concatenate(item))) == len(np.concatenate(item)):
+                sorted_seq = sorted(list(chain(*item)))
+                gaps = missing_elements(sorted_seq)
+                completeness = len(sorted_seq) / (len(sorted_seq) + len(gaps))
+                if completeness >= min_compl:
+                    fused_set_uniq.add(item)
+        final_set = set(ori_set.union(fused_set_uniq))
+
+    return final_set
+
+
+def recombine_groups(group_indx, min_r_length=1, max_r_length=3):
+    """
+    Generate a set of all possible combinations for each group of indices
+    {((1, 2), (5,)), (1, 2), (5,)} -> [[1, 2], [1], [2], [5], [1, 2, 5], [1, 5], [2, 5]]
+    :param group_indx: set of tuples, output of assemble_groups
+    :param min_r_length: minimum length of combinations
+    :param max_r_length: maximum length of combinations
+    :return: list of lists of indices
+    """
+    return_list = []
+    for group in group_indx:
+        for r_length in range(min_r_length, max_r_length):
+            try:
+                new_comb = sorted(list(np.concatenate(group)))
+            except:
+                new_comb = list(group)
+            if new_comb not in return_list:
+                return_list.append(new_comb)
+            for new_mix_comb in list(combinations(new_comb, r_length)):
+                new_mix_comb = sorted(new_mix_comb)
+                if new_mix_comb not in return_list:
+                    return_list.append(list(new_mix_comb))
+    return return_list
+
+
+def permute_leave_one_out(original_list, min_terms=1):
+    """
+    Supplement lists of iterables with groups where each of the items is left out
+    :param original_list:
+    :param min_terms:
+    :return: supplemented list
+    [['1','2','3']] -> [['1', '2', '3'], ['2', '3'], ['1', '3'], ['1', '2']]
+    """
+    permuted_list = original_list.copy()
+    for group in original_list:
+        if len(group) <= min_terms:
+            continue
+        for i, x in enumerate(group):
+            permuted_group = group.copy()
+            del permuted_group[i]
+            if permuted_group not in permuted_list:
+                permuted_list.append(permuted_group)
+    return permuted_list
+
+
+def self_evaluation(correct_spelling=False, resolve_conflicts=False):
     total = 0
     correct = 0
     wrong = []
@@ -154,33 +454,20 @@ def self_evaluation(correct_spelling=False):
     logger.info('Running self evaluation, this may take a few minutes \n')
     i = 0
     n_nodes = len(hpo_network.nodes)
-
+    ext = Extractor(correct_spelling=correct_spelling, remove_overlapping=True, resolve_conflicts=True)
     for node in hpo_network:
         total += 1
         term = hpo_network.nodes[node]['name']
         hpids = []
-        extracted = hpo(term, correct_spelling=correct_spelling)
-        if extracted:
-            extracted = json.loads(extracted)
-
-            for item in extracted:
-                hpids += item['hpid']
-
-            if str(node) in hpids:
-                correct += 1
-            else:
-                wrong.append(dict(
-                    actual=node,
-                    actual_name=term,
-                    extracted=hpids,
-                    extracted_name=[hpo_network.nodes[x]['name'] for x in hpids],
-                ))
+        extracted = ext.hpo(term).hpids
+        if str(node) in extracted:
+            correct += 1
         else:
             wrong.append(dict(
                 actual=node,
                 actual_name=term,
-                extracted=[],
-                extracted_name=[],
+                extracted=hpids,
+                extracted_name=[hpo_network.nodes[x]['name'] for x in hpids],
             ))
 
         i += 1
@@ -189,6 +476,5 @@ def self_evaluation(correct_spelling=False):
 
     logger.info('Done \n')
 
-    logger.info(f"{(correct/total)*100} percent correct, {len(wrong)} items wrong")
+    logger.info(f"{(correct / total) * 100} percent correct, {len(wrong)} items wrong")
     return wrong
-
diff --git a/txt2hpo/nlp.py b/txt2hpo/nlp.py
index 7fb2a31..b348919 100644
--- a/txt2hpo/nlp.py
+++ b/txt2hpo/nlp.py
@@ -1,23 +1,72 @@
 import spacy
+import en_core_sci_sm
+from negspacy.negation import Negex
+from gensim.parsing.preprocessing import remove_stopwords
 from txt2hpo.config import logger
+from txt2hpo.util import hpo_network
 from nltk.stem import RegexpStemmer
+from spacy.tokens import Token
 
 
+def nlp_model(negation_language="en"):
+    try:
+        nlp = en_core_sci_sm.load(disable=["tagger", "parser"])
+        nlp.add_pipe(nlp.create_pipe('sentencizer'))
+        negex = Negex(nlp, language=negation_language, chunk_prefix=["no"])
+        nlp.add_pipe(negex, last=True)
+        Token.set_extension('negex', default=False, force=True)
+
+    except OSError:
+        nlp = None
+        logger.info('Negation model could not be loaded\n')
+
+    if nlp:
+        for not_a_stop in remove_from_stops.split(" "):
+            nlp.vocab[not_a_stop].is_stop = False
+            nlp.vocab[not_a_stop.capitalize()].is_stop = False
+
+    return nlp
+
 try:
-    nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner"])
-except OSError:
-    logger.info('Performing a one-time download of an English language model for the spaCy POS tagger\n')
-    from spacy.cli import download
-    download('en')
-    nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner"])
+    nlp_sans_ner = en_core_sci_sm.load(disable=["tagger", "parser", "ner"])
+    logger.info('Using sci spacy language model\n')
 
+except OSError as e:
+    logger.info('Sci spacy language model could not be loaded\n')
+    logger.info('Performing a one-time download of an English language model\n')
+    from spacy.cli import download
+    download('en_core_web_sm')
+    nlp_sans_ner = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner"])
 
 # these are used in hpo as part of phenotype definition, should block from filtering
-remove_from_stops = "first second third fourth fifth under over front back behind ca below without no not "
-remove_from_stops += "out up side right left more less during than take move full"
+remove_from_stops = "first second third fourth fifth under over front back behind ca above below without no not "
+remove_from_stops += "out up side right left more less during than take move full few all to"
+
 for not_a_stop in remove_from_stops.split(" "):
-    nlp.vocab[not_a_stop].is_stop = False
-    nlp.vocab[not_a_stop.capitalize()].is_stop = False
+    nlp_sans_ner.vocab[not_a_stop].is_stop = False
+    nlp_sans_ner.vocab[not_a_stop.capitalize()].is_stop = False
+
 
 st = RegexpStemmer('ing$|e$|able$|ic$|ia$|ity$|al$|ly$', min=7)
 
+
+def similarity_term_to_context(term, context, model):
+    """
+    Score similarity (term|context)
+    :param term: hpo term
+    :param context: context of term
+    :param model: doc2vec model used to score term given context
+    :return: float
+    """
+    def remove_out_of_vocab(tokens):
+        return [x for x in tokens if x in model.vocab]
+
+    hpo_term = hpo_network.nodes[term]
+    hpo_term_definition = hpo_term['name']
+    term_tokens = remove_out_of_vocab(remove_stopwords(hpo_term_definition).split())
+    context_tokens = remove_out_of_vocab(remove_stopwords(context).split())
+    if term_tokens and context_tokens:
+        sim = model.n_similarity(term_tokens, context_tokens)
+    else:
+        sim = -0.999
+    return sim
diff --git a/txt2hpo/spellcheck.py b/txt2hpo/spellcheck.py
index cf1e516..fe5ba09 100644
--- a/txt2hpo/spellcheck.py
+++ b/txt2hpo/spellcheck.py
@@ -1,18 +1,17 @@
 
 # Peter Norvig spell checker https://norvig.com/spell-correct.html
-import spacy
+from txt2hpo.data import load_spellcheck_vocab
+from txt2hpo.nlp import nlp_sans_ner
 
-from txt2hpo.data.spellcheck_vocab import spellcheck_vocab
+spellcheck_vocab = load_spellcheck_vocab()
 
 
-nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner"])
-
 def P(word, N=sum(spellcheck_vocab.values())):
     "Probability of `word`."
     if word in spellcheck_vocab:
         return spellcheck_vocab[word] / N
     else:
-        return 0.1/N
+        return 0
 
 
 def correction(word):
@@ -50,7 +49,7 @@ def spellcheck(text):
     "correct spelling in a sentence"
     # clean up text from punctuation marks
     corrected_text = []
-    for token in nlp(text):
+    for token in nlp_sans_ner(text):
 
         if token.is_stop:
             corrected_text.append(token.text_with_ws)
@@ -58,8 +57,16 @@ def spellcheck(text):
         elif token.is_punct:
             corrected_text.append(token.text_with_ws)
 
+        elif len(token) < 5:
+            corrected_text.append(token.text_with_ws)
         else:
-            corrected_text.append(correction(token.text))
+            corrected = correction(token.text.lower())
+            if token.text[0].isupper() and token.text[-1].islower():
+                corrected_text.append(corrected.capitalize())
+            elif token.text[0].isupper() and token.text[-1].isupper():
+                corrected_text.append(corrected.upper())
+            else:
+                corrected_text.append(corrected)
             corrected_text.append(token.whitespace_)
 
     return "".join(corrected_text)
\ No newline at end of file
diff --git a/txt2hpo/summarize.py b/txt2hpo/summarize.py
new file mode 100644
index 0000000..8ea7cdf
--- /dev/null
+++ b/txt2hpo/summarize.py
@@ -0,0 +1,91 @@
+
+import json
+import pandas as pd
+import numpy as np
+from txt2hpo.util import group_pairs, summarize_tuples, df_from_tuples
+from txt2hpo.config import logger
+from phenopy.util import half_product
+from functools import reduce
+
+
+def phenotype_distance(extracted_hpos):
+    """
+    Given the return from hpo, find the normalized distance between all terms in the document.
+    This could serve as a proxy for cooccurrence.
+    :param extracted_hpos: json string output of txt2hpo
+    :return: list of tuples (hpo1, hpo2, distance)
+    """
+
+    # load the extracted phenotypes into a pandas DataFrame
+    hpo_ids = json.loads(extracted_hpos)
+    df = pd.DataFrame(hpo_ids)
+    # make two rows for text offsets where more than one HPO term was identified
+    df = df.explode('hpid').reset_index(drop=True)
+    # location is the starting index of an HPO term in the document.
+    df['location'] = df['index'].apply(min)
+
+    # use max_idx as a normalization factor (a proxy for how long the document is)
+    # TODO:
+    # Maybe include the length of the text in the json object as a top-level key.
+    max_idx = df['location'].max()
+
+    # loop through the combinations and the diagonal, collect distances b/w each pair
+    phenotype_pairs = []
+    for hpo_pair in half_product(len(df), len(df)):
+        x, y = sorted(hpo_pair, reverse=True)
+        distance = abs(df.iloc[x]['location'] - df.iloc[y]['location']) / max_idx
+        phenotype_pairs.append((df.iloc[x]['hpid'], df.iloc[y]['hpid'], distance))
+
+    return phenotype_pairs
+
+
+def distances(array_of_extracted_hpos, min_n_distances=2, summary_method='mean'):
+    """
+    Summarize array of phenotype distances generated from parsing distinct documents
+    :param array_of_extracted_hpos: array of phenotype distances
+    :param min_n_distances: minimum number of coocurances / distances to summarize to filter
+    :param summary_method: (mean,min) how to summarize coocurances / distances
+    :return: pandas dataframe
+    """
+    dfs = []
+    if summary_method not in ['mean', 'min']:
+        logger.critical(f'Summary method undefined: {summary_method} ')
+        return dfs
+    # process distances of each document into df
+    for extracted_hpo in array_of_extracted_hpos:
+
+        # get term-term pairwise distances for each document
+        phenotype_pairs = phenotype_distance(extracted_hpo)
+
+        # find identical pairs and combine their values
+        grouped_pairs = group_pairs(phenotype_pairs)
+
+        # get summary metric for each term pair
+        summarized_pairs = summarize_tuples(grouped_pairs)
+
+        # make a dataframe and append it to list
+        tup_df = df_from_tuples(summarized_pairs)
+
+        dfs.append(tup_df)
+
+    # merge dataframes
+    dfs_merged = reduce(lambda x, y: pd.merge(x, y, on=['idx1', 'idx2'], how="outer"), dfs)
+
+    dfs_merged['values'] = dfs_merged.values.tolist()
+    # remove nan from list of distance values
+    dfs_merged['values'] = dfs_merged['values'].apply(lambda x: [v for v in x if ~np.isnan(v)])
+
+    # keep relationships with at least 3 distance values
+    dfs_merged['n'] = dfs_merged['values'].apply(len)
+    dfs_merged = dfs_merged[dfs_merged['n'] > min_n_distances]
+
+    # summarize
+    if summary_method == 'mean':
+        dfs_merged['mean_score'] = dfs_merged['values'].apply(np.mean)
+        dfs_merged = dfs_merged[['mean_score', 'n']]
+
+    elif summary_method == 'min':
+        dfs_merged['min_score'] = dfs_merged['values'].apply(np.min)
+        dfs_merged = dfs_merged[['min_score', 'n']]
+
+    return dfs_merged.reset_index()
diff --git a/txt2hpo/util.py b/txt2hpo/util.py
new file mode 100644
index 0000000..c7c552a
--- /dev/null
+++ b/txt2hpo/util.py
@@ -0,0 +1,59 @@
+import pandas as pd
+
+import math
+from phenopy.config import config as phenopy_config
+from phenopy import generate_annotated_hpo_network
+
+
+obo_file = phenopy_config.get('hpo', 'obo_file')
+
+disease_to_phenotype_file = phenopy_config.get('hpo', 'disease_to_phenotype_file')
+
+hpo_network, alt2prim, disease_records = \
+    generate_annotated_hpo_network(obo_file,
+                                   disease_to_phenotype_file,
+                                   annotations_file=None,
+                                   ages_distribution_file=None
+                                   )
+
+def group_pairs(phenotype_pairs):
+    """group unique keys and combine their values"""
+    unique_pairs = {}
+    for k1,k2,value in phenotype_pairs:
+        sorted_pair = tuple(sorted([k1,k2]))
+        if sorted_pair not in unique_pairs:
+            unique_pairs[sorted_pair]=[value]
+        else:
+            if value not in unique_pairs[sorted_pair]:
+                unique_pairs[sorted_pair].append(value)
+    return unique_pairs
+
+
+def summarize_tuples(pairs, how='min'):
+    """summarize collection of tuple with list values"""
+    summarized_pairs = []
+    for k,v in pairs.items():
+        if how == 'min':
+            summarized_value = min(v)
+        elif how == 'max':
+            summarized_value = max(v)
+        elif how == 'mean':
+            summarized_value = math.mean(v)
+        summarized_pairs.append((k[0], k[1], summarized_value))
+    return summarized_pairs
+
+
+def df_from_tuples(tuples):
+    """make pandas df from tuples"""
+    tup_df = pd.DataFrame(tuples, columns=['term_a','term_b','score'])
+    tup_df.index = pd.MultiIndex.from_arrays(tup_df[['term_a', 'term_b']].values.T, names=['idx1', 'idx2'])
+    tup_df = tup_df[['score']]
+    return tup_df
+
+
+def remove_key(dict_list, key):
+    """remove a key from each dict in a list of dictionaries"""
+    for d in dict_list:
+        if key in d:
+            del d[key]
+    return dict_list