Exploring semi-supervised coreference resolution of medical concepts using semantic and temporal features

Preethi Raghavan, Eric Fosler-Lussier, Albert M. Lai

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

7 Scopus citations

Abstract

We investigate the task of medical concept coreference resolution in clinical text using two semi-supervised methods, co-training and multi-view learning with posterior regularization. By extracting semantic and temporal features of medical concepts found in clinical text, we create conditionally independent data views; co-training MaxEnt classifiers on this data works almost as well as supervised learning for the task of pairwise coreference resolution of medical concepts. We also train Max- Ent models with expectation constraints, using posterior regularization, and find that posterior regularization performs comparably to or slightly better than co-training. We describe the process of semantic and temporal feature extraction and demonstrate our methods on a corpus of case reports from the New England Journal of Medicine and a corpus of patient narratives obtained from The Ohio State University Wexner Medical Center.

Original languageEnglish
Title of host publicationNAACL HLT 2012 - 2012 Conference of the North American Chapter of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages731-741
Number of pages11
ISBN (Electronic)1937284204, 9781937284206
StatePublished - 2012
Event2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2012 - Montreal, Canada
Duration: Jun 3 2012Jun 8 2012

Publication series

NameNAACL HLT 2012 - 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference

Conference

Conference2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2012
Country/TerritoryCanada
CityMontreal
Period06/3/1206/8/12

Fingerprint

Dive into the research topics of 'Exploring semi-supervised coreference resolution of medical concepts using semantic and temporal features'. Together they form a unique fingerprint.

Cite this