# An Information Retrieval and Extraction Tool for Covid-19 Related Papers

arXiv: https://arxiv.org/abs/2401.16430 · HTML: https://mlsanity.com/paper/2401.16430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 20 Jan 2024.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [SECTOR: A Neural Model for Coherent Topic Segmentation and Classification](https://mlsanity.com/paper/1902.04793/) (2019)
- [BioSentVec: creating sentence embeddings for biomedical texts](https://mlsanity.com/paper/1810.09302/) (2018)
- [ktrain: A Low-Code Library for Augmented Machine Learning](https://mlsanity.com/paper/2004.10703/) (2020)
- [COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature](https://mlsanity.com/paper/2007.12731/) (2020)
- [Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision](https://mlsanity.com/paper/2003.12218/) (2020)
- [CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization](https://mlsanity.com/paper/2006.09595/) (2020)
- [Automatic Generation of Topic Labels](https://mlsanity.com/paper/2006.00127/) (2020)
- [Experiment Segmentation in Scientific Discourse as Clause-level Structured Prediction using Recurrent Neural Networks](https://mlsanity.com/paper/1702.05398/) (2017)
- [Segmenting Scientific Abstracts into Discourse Categories: A Deep Learning-Based Approach for Sparse Labeled Data](https://mlsanity.com/paper/2005.05414/) (2020)
- [CODA-19: Using a Non-Expert Crowd to Annotate Research Aspects on 10,000+ Abstracts in the COVID-19 Open Research Dataset](https://mlsanity.com/paper/2005.02367/) (2020)
- [COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research](https://mlsanity.com/paper/2008.07880/) (2020)
- [Target specific mining of COVID-19 scholarly articles using one-class approach](https://mlsanity.com/paper/2004.11706/) (2020)

## Abstract (arXiv)

Background: The COVID-19 pandemic has caused severe impacts on health systems worldwide. Its critical nature and the increased interest of individuals and organizations to develop countermeasures to the problem has led to a surge of new studies in scientific journals. Objetive: We sought to develop a tool that incorporates, in a novel way, aspects of Information Retrieval (IR) and Extraction (IE) applied to the COVID-19 Open Research Dataset (CORD-19). The main focus of this paper is to provide researchers with a better search tool for COVID-19 related papers, helping them find reference papers and hightlight relevant entities in text. Method: We applied Latent Dirichlet Allocation (LDA) to model, based on research aspects, the topics of all English abstracts in CORD-19. Relevant named entities of each abstract were extracted and linked to the corresponding UMLS concept. Regular expressions and the K-Nearest Neighbors algorithm were used to rank relevant papers. Results: Our tool has shown the potential to assist researchers by automating a topic-based search of CORD-19 papers. Nonetheless, we identified that more fine-tuned topic modeling parameters and increased accuracy of the research aspect classifier model could lead to a more accurate and reliable tool. Conclusion: We emphasize the need of new automated tools to help researchers find relevant COVID-19 documents, in addition to automatically extracting useful information contained in them. Our work suggests that combining different algorithms and models could lead to new ways of browsing COVID-19 paper data.