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ItsdbTreebanking_ItsdbModeling
If you have treebanked a profile, and have Rob Malouf's maxent package (in particular the program estimate) installed, then you can train a scoring model which pet (PetTop) can use.
Select the treebanked profile (left-click), or profiles (click in the radio buttons) and then select Trees | Train from the menus. It will prompt you for the filename to put the scoring model in. The tradtion is something like corpus-version.mem. You should have the grammar used for treebanking loaded into the LKB (LkbTop). Training is normally fairly fast.
The scoring model is referenced in cheap's grammar.set:
;;;
;;; scoring mechanism (fairly embryonic, for now)
;;;
sm := "hinoki-050417.mem".
You can compare the ranking of a given profile with a treebanked gold standard (assuming the same test-suite and grammar). The ranking can be changed by changing the scoring model in the parser.
To compare: select the gold standard (middle click), then the profile to be scored as the current database (left click); (make sure the current version of the grammar is loaded into the LKB).
Set: Trees | Switches | Implicit Ranks and Trees | Switches | Result Equivalence; and then go Trees | Score.
;; score results in .data. against ground truth in .gold. operates in
;; several slightly distinct modes: (i) using the implicit parse ranking in
;; the order of `results' or (ii) using an explicit ranking from the `score'
;; relation an orthogonal dimension of variation is (a) scoring by result
;; identifier (e.g. within the same profile or against one that is comprised
;; of identical results) vs. (b) scoring by derivation equivalence (e.g.
;; when comparing best-first parser output against a gold standard).
In order to make the scoring faster, you should do a thinning normalize on the gold profile for comparison first. This thins (implicitly) to only those trees marked as good by the annotator, i.e. you thin out all dis-preferred trees. To get a 5-best comparison, play with the Scoring Beam value.
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