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,
- Editors:
- Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 296–301
- Language:
- URL:
- https://aclanthology.org/W17-2337
- DOI:
- 10.18653/v1/W17-2337
- Cite (ACL):
- Mourad Sarrouti and Said Ouatik El Alaoui. 2017. A Biomedical Question Answering System in BioASQ 2017. In BioNLP 2017, pages 296–301, Vancouver, Canada,. Association for Computational Linguistics.
- Cite (Informal):
- A Biomedical Question Answering System in BioASQ 2017 (Sarrouti & Ouatik El Alaoui, BioNLP 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/W17-2337.pdf
- Data
- BioASQ