Document Retrieval for Biomedical Question Answering with Neural Sentence Matching

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Document retrieval (DR) forms an important component in end-to-end question-answering (QA) systems where particular answers are sought for well-formed questions. DR in the QA scenario is also useful by itself even without a more involved natural language processing component to extract exact answers from the retrieved documents. This latter step may simply be done by humans like in traditional search engines granted the retrieved documents contain the answer. In this paper, we take advantage of datasets made available through the BioASQ end-to-end QA shared task series and build an effective biomedical DR system that relies on relevant answer snippets in the BioASQ training datasets. At the core of our approach is a question-answer sentence matching neural network that learns a measure of relevance of a sentence to an input question in the form of a matching score. In addition to this matching score feature, we also exploit two auxiliary features for scoring document relevance: the name of the journal in which a document is published and the presence/absence of semantic relations (subject-predicate-object triples) in a candidate answer sentence connecting entities mentioned in the question. We rerank our baseline sequential dependence model scores using these three additional features weighted via adaptive random research and other learning-to-rank methods. Our full system placed 2nd in the final batch of Phase A (DR) of task B (QA) in BioASQ 2018. Our ablation experiments highlight the significance of the neural matching network component in the full system.

Original languageEnglish
Title of host publicationProceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
EditorsM. Arif Wani, Mehmed Kantardzic, Moamar Sayed-Mouchaweh, Joao Gama, Edwin Lughofer
Pages194-201
Number of pages8
ISBN (Electronic)9781538668047
DOIs
StatePublished - Jul 2 2018
Event17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 - Orlando, United States
Duration: Dec 17 2018Dec 20 2018

Publication series

NameProceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018

Conference

Conference17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
Country/TerritoryUnited States
CityOrlando
Period12/17/1812/20/18

Bibliographical note

Funding Information:
ACKNOWLEDGEMENT This research is supported by the U.S. National Library of Medicine through grant R21LM012274. We also gratefully acknowledge the support of the NVIDIA Corporation for its donation of the Titan X Pascal GPU used for this research. We thank anonymous reviewers for their helpful comments.

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Information retrieval
  • Learning-to-rank
  • Machine reading
  • Natural language processing
  • Question answering
  • Sentence matching

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Safety, Risk, Reliability and Quality
  • Signal Processing
  • Decision Sciences (miscellaneous)

Fingerprint

Dive into the research topics of 'Document Retrieval for Biomedical Question Answering with Neural Sentence Matching'. Together they form a unique fingerprint.

Cite this