The SkimLit NLP Project is an advanced Natural Language Processing (NLP) model designed to analyze scientific abstracts. Its goal is to classify and extract meaningful tags from research papers' abstracts, making it easier to understand the core contributions of each study.
- Preprocessing of scientific text data.
- Implementation of tokenization, normalization, and text augmentation techniques.
- A robust NLP model built using state-of-the-art libraries.
- Evaluation metrics to ensure high accuracy and performance.
Scientific literature is growing at an unprecedented pace, making it challenging for researchers to stay updated. This project automates the classification of research abstracts, enabling faster comprehension and organization of academic content.
- Python
- TensorFlow/Keras
- Natural Language Processing (NLP) techniques
This project can be extended for:
- Automating literature reviews.
- Building smarter search engines for academic articles.
- Categorizing and summarizing scientific content.
Inspired by the challenges of understanding and organizing large-scale scientific data. Special thanks to the NLP community for providing the resources and inspiration for this project.