J Am Med Inform Assoc - Knowledge-based biomedical word sense disambiguation: an evaluation and application to clinical document classification.

Tópicos

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Resumo

CKGROUND: Word sense disambiguation (WSD) methods automatically assign an unambiguous concept to an ambiguous term based on context, and are important to many text-processing tasks. In this study we developed and evaluated a knowledge-based WSD method that uses semantic similarity measures derived from the Unified Medical Language System (UMLS) and evaluated the contribution of WSD to clinical text classification.METHODS: We evaluated our system on biomedical WSD datasets and determined the contribution of our WSD system to clinical document classification on the 2007 Computational Medicine Challenge corpus.RESULTS: Our system compared favorably with other knowledge-based methods. Machine learning classifiers trained on disambiguated concepts significantly outperformed those trained using all concepts.CONCLUSIONS: We developed a WSD system that achieves high disambiguation accuracy on standard biomedical WSD datasets and showed that our WSD system improves clinical document classification.DATA SHARING: We integrated our WSD system with MetaMap and the clinical Text Analysis and Knowledge Extraction System, two popular biomedical natural language processing systems. All codes required to reproduce our results and all tools developed as part of this study are released as open source, available under http://code.google.com/p/ytex.

Resumo Limpo

ckground word sens disambigu wsd method automat assign unambigu concept ambigu term base context import mani textprocess task studi develop evalu knowledgebas wsd method use semant similar measur deriv unifi medic languag system uml evalu contribut wsd clinic text classificationmethod evalu system biomed wsd dataset determin contribut wsd system clinic document classif comput medicin challeng corpusresult system compar favor knowledgebas method machin learn classifi train disambigu concept signific outperform train use conceptsconclus develop wsd system achiev high disambigu accuraci standard biomed wsd dataset show wsd system improv clinic document classificationdata share integr wsd system metamap clinic text analysi knowledg extract system two popular biomed natur languag process system code requir reproduc result tool develop part studi releas open sourc avail httpcodegooglecompytex

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