A Biomedical Question Answering System in BioASQ 2017

Mourad Sarrouti, Said Ouatik El Alaoui


Abstract
Question answering, the identification of short accurate answers to users questions, is a longstanding challenge widely studied over the last decades in the open domain. However, it still requires further efforts in the biomedical domain. In this paper, we describe our participation in phase B of task 5b in the 2017 BioASQ challenge using our biomedical question answering system. Our system, dealing with four types of questions (i.e., yes/no, factoid, list, and summary), is based on (1) a dictionary-based approach for generating the exact answers of yes/no questions, (2) UMLS metathesaurus and term frequency metric for extracting the exact answers of factoid and list questions, and (3) the BM25 model and UMLS concepts for retrieving the ideal answers (i.e., paragraph-sized summaries). Preliminary results show that our system achieves good and competitive results in both exact and ideal answers extraction tasks as compared with the participating systems.
Anthology ID:
W17-2337
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
296–301
Language:
URL:
https://www.aclweb.org/anthology/W17-2337
DOI:
10.18653/v1/W17-2337
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2337.pdf