This repository contains work done as part of AI-3 course by Univ.ai.
Our team - Himanshu, Aayush, Srish
Extract key information from scientific papers using NER
- The number of scientific papers published per year has exploded in recent years, strengthening its value as one of the main drivers for scientific progress.
- In astronomy alone, more than 41,000 new articles are published every year and the vast majority are available either via an open-access model or via pre-print services.
- Indexing the article’s full-text in search engines helps discover and retrieve vital scientific information to continue building on the shoulders of giants, informing policy, and making evidence-based decisions.
- Nevertheless, it is difficult to navigate in this ocean of data; finding articles rely heavily on string matching searches and following citations/references.
- NER helps us extract key information from scientific papers which can help search engines to better select and filter articles.
- The dataset used in this project is from Workshop on Information Extraction from Scientific Publications (WIESP/2022).
- Following poster shows our methodology and results in breif.
- Notebook used for modelling.
- Files used for streamlit demo.
- Presentation slides for more detailed explaination.
- Video presentation along with app demo.