Eli Laird
SMU AI Lab
[email protected]
This repo contains our efforts to build Natural Language Processing tools to assist researchers around the world with processing ~51,000 research papers related to COVID-19 and similar coronaviruses. All work was done my several undergraduate members of the SMU AI Lab with the guidance of SMU AI Lab faculty.
Note: This repo is a work in progress. The documentation is still light and unorganized. Please be patient for a final release of our work.
The dataset used in this repo is provided by the Allen Institute For AI and posted on Kaggle.com. The dataset can be found here: https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge
- Description: Every row in this csv contains the words corresponding to each topic
- Link: https://smu.box.com/s/q3jos6b2mqml2mr85zmyhxffx8jz82qu
- Description: This is the LDA model used to generate the 25 topics in binary form. This can be loaded using the Pickle package in Python
- Link: https://smu.box.com/s/uqbbw8doy724kpqxjn17x6c5jpdyg59g
- Description: This dataframe contains the bhattacharya distances between every task-document and corpus document
- Link: https://smu.box.com/s/eltmk2dphlataps24hol2hilogo265pv
- Description: Metadata for bjattacharya_similarity_matrix.csv
- 'Corpus' : Describes the corpus each document comes from * Values: {'main', 'task'}
- 'Doc_Index' : Index for document in corresponding corpus
- 'Topic' : Topic assignment for document
- Link: https://smu.box.com/s/tflz9uyoul3yg3jbheynilce3lma5x35