Loading...
Automatic question answering for medical MCQs: Can it go further than information retrieval?
Ha, Le An Yaneva, Viktoriya
Ha, Le An
Yaneva, Viktoriya
Authors
Editors
Other contributors
Affiliation
Epub Date
Issue Date
2019-09-04
Submitted date
Subjects
Alternative
Abstract
We present a novel approach to automatic question answering that does not depend on the performance of an information retrieval (IR) system and does not require training data. We evaluate the system performance on a challenging set of university-level medical science multiple-choice questions. Best performance is achieved when combining a neural approach with an IR approach, both of which work independently. Unlike previous approaches, the system achieves statistically significant improvement over the random guess baseline even for questions that are labeled as challenging based on the performance of baseline solvers.
Citation
Ha, L. A. and Yaneva, V. (2019) Automatic question answering for medical MCQs: Can it go further than information retrieval? Proceedings of Recent Advances in Natural Language Processing, Varna, Bulgaria, pp. 418-422.
Publisher
Journal
Research Unit
DOI
PubMed ID
PubMed Central ID
Embedded videos
Additional Links
Type
Conference contribution
Language
en
Description
Series/Report no.
ISSN
1313-8502
EISSN
ISBN
9789544520557