| Applying semantic-based probabilistic context-free grammar to medical language processing - A preliminary study on parsing medication sentences. | |
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MedLine Citation:
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PMID: 21856440 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Semantic-based sublanguage grammars have been shown to be an efficient method for medical language processing. However, given the complexity of the medical domain, parsers using such grammars inevitably encounter ambiguous sentences, which could be interpreted by different groups of production rules and consequently result in two or more parse trees. One possible solution, which has not been extensively explored previously, is to augment productions in medical sublanguage grammars with probabilities to resolve the ambiguity. In this study, we associated probabilities with production rules in a semantic-based grammar for medication findings and evaluated its performance on reducing parsing ambiguity. Using the existing data set from 2009 i2b2 NLP (Natural Language Processing) challenge for medication extraction, we developed a semantic-based CFG (Context Free Grammar) for parsing medication sentences and manually created a Treebank of 4564 medication sentences from discharge summaries. Using the Treebank, we derived a semantic-based PCFG (Probabilistic Context Free Grammar) for parsing medication sentences. Our evaluation using a 10-fold cross validation showed that the PCFG parser dramatically improved parsing performance when compared to the CFG parser. |
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Authors:
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Hua Xu; Samir Abdelrahman; Yanxin Lu; Joshua C Denny; Son Doan |
Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-8-12 |
Journal Detail:
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Title: Journal of biomedical informatics Volume: - ISSN: 1532-0480 ISO Abbreviation: - Publication Date: 2011 Aug |
Date Detail:
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Created Date: 2011-8-22 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 100970413 Medline TA: J Biomed Inform Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Copyright Information:
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Copyright © 2011. Published by Elsevier Inc. |
Affiliation:
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Department of Biomedical Informatics, Vanderbilt University, School of Medicine, Nashville, TN, USA. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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