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Medical Literature Mining and Retrieval in a Conversational Setting

2021-07-23 23:02:59
Souvik Das, Sougata Saha, Rohini K. Srihari
arXiv_CL
           
Abstract

The Covid-19 pandemic has caused a spur in the medical research literature. With new research advances in understanding the virus, there is a need for robust text mining tools which can process, extract and present answers from the literature in a concise and consumable way. With a DialoGPT based multi-turn conversation generation module, and BM-25 \& neural embeddings based ensemble information retrieval module, in this paper we present a conversational system, which can retrieve and answer coronavirus-related queries from the rich medical literature, and present it in a conversational setting with the user. We further perform experiments to compare neural embedding-based document retrieval and the traditional BM25 retrieval algorithm and report the results.

Abstract (translated)

URL

https://arxiv.org/abs/2108.01436

PDF

https://arxiv.org/pdf/2108.01436.pdf

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