Information Extraction From Scientific Publication

This repository contains work done as part of AI-3 course by Univ.ai.
Our team - Himanshu, Aayush, Srish

Project

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.

Data

• The dataset used in this project is from Workshop on Information Extraction from Scientific Publications (WIESP/2022).

- It comprises of text fragments from astrophysics papers, provided by the NASA Astrophysical Data System with manually tagged astronomical facilities and other entities of interest (e.g., celestial objects). - [Click Here!](https://huggingface.co/datasets/adsabs/WIESP2022-NER) to access the data.

Methodology and Result

• 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.