Medical Literature Mining and Retrieval in a Conversational Setting

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.

arXiv commentSBP-BRiMS 2020 Pandemic Track paper

References in corpus (6)

• BioBERT: a pre-trained biomedical language representation model for biomedical text mining
• DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
• CORD-19: The COVID-19 Open Research Dataset
• Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations
• Answering Questions on COVID-19 in Real-Time
• Emora: An Inquisitive Social Chatbot Who Cares For You