Decomposing Consumer Health Questions

Kirk Roberts, Halil Kilicoglu, Marcelo Fiszman, Dina Demner-Fushman

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper presents a method for decomposing long, complex consumer health questions. Our approach largely decomposes questions using their syntactic structure, recognizing independent questions embedded in clauses, as well as coordinations and exemplifying phrases. Additionally, we identify elements specific to disease-related consumer health questions, such as the focus disease and background information. To achieve this, our approach combines rank-and-filter machine learning methods with rule-based methods. Our results demonstrate significant improvements over the heuristic methods typically employed for question decomposition that rely only on the syntactic parse tree.

Original languageEnglish (US)
Title of host publicationACL 2014 - BioNLP 2014, Workshop on Biomedical Natural Language Processing, Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Number of pages9
ISBN (Electronic)9781941643181
StatePublished - 2014
Externally publishedYes
EventACL 2014 Workshop on Biomedical Natural Language Processing, BioNLP 2014 - Baltimore, United States
Duration: Jun 27 2014Jun 28 2014

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X


ConferenceACL 2014 Workshop on Biomedical Natural Language Processing, BioNLP 2014
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics


Dive into the research topics of 'Decomposing Consumer Health Questions'. Together they form a unique fingerprint.

Cite this