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 publicationProceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies
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