Assessing the performance of Olelo, a real-time biomedical question answering application

Mariana Neves, Fabian Eckert, Hendrik Folkerts, Matthias Uflacker


Abstract
Question answering (QA) can support physicians and biomedical researchers to find answers to their questions in the scientific literature. Such systems process large collections of documents in real time and include many natural language processing (NLP) procedures. We recently developed Olelo, a QA system for biomedicine which includes various NLP components, such as question processing, document and passage retrieval, answer processing and multi-document summarization. In this work, we present an evaluation of our system on the the fifth BioASQ challenge. We participated with the current state of the application and with an extension based on semantic role labeling that we are currently investigating. In addition to the BioASQ evaluation, we compared our system to other on-line biomedical QA systems in terms of the response time and the quality of the answers.
Anthology ID:
W17-2344
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
342–350
Language:
URL:
https://www.aclweb.org/anthology/W17-2344
DOI:
10.18653/v1/W17-2344
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2344.pdf
Poster:
 W17-2344.Poster.pdf