J Am Med Inform Assoc - Word sense disambiguation in the clinical domain: a comparison of knowledge-rich and knowledge-poor unsupervised methods.

Tópicos

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Resumo

JECTIVE: To evaluate state-of-the-art unsupervised methods on the word sense disambiguation (WSD) task in the clinical domain. In particular, to compare graph-based approaches relying on a clinical knowledge base with bottom-up topic-modeling-based approaches. We investigate several enhancements to the topic-modeling techniques that use domain-specific knowledge sources.MATERIALS AND METHODS: The graph-based methods use variations of PageRank and distance-based similarity metrics, operating over the Unified Medical Language System (UMLS). Topic-modeling methods use unlabeled data from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) database to derive models for each ambiguous word. We investigate the impact of using different linguistic features for topic models, including UMLS-based and syntactic features. We use a sense-tagged clinical dataset from the Mayo Clinic for evaluation.RESULTS: The topic-modeling methods achieve 66.9% accuracy on a subset of the Mayo Clinic's data, while the graph-based methods only reach the 40-50% range, with a most-frequent-sense baseline of 56.5%. Features derived from the UMLS semantic type and concept hierarchies do not produce a gain over bag-of-words features in the topic models, but identifying phrases from UMLS and using syntax does help.DISCUSSION: Although topic models outperform graph-based methods, semantic features derived from the UMLS prove too noisy to improve performance beyond bag-of-words.CONCLUSIONS: Topic modeling for WSD provides superior results in the clinical domain; however, integration of knowledge remains to be effectively exploited.

Resumo Limpo

jectiv evalu stateoftheart unsupervis method word sens disambigu wsd task clinic domain particular compar graphbas approach reli clinic knowledg base bottomup topicmodelingbas approach investig sever enhanc topicmodel techniqu use domainspecif knowledg sourcesmateri method graphbas method use variat pagerank distancebas similar metric oper unifi medic languag system uml topicmodel method use unlabel data multiparamet intellig monitor intens care mimic ii databas deriv model ambigu word investig impact use differ linguist featur topic model includ umlsbas syntact featur use sensetag clinic dataset mayo clinic evaluationresult topicmodel method achiev accuraci subset mayo clinic data graphbas method reach rang mostfrequentsens baselin featur deriv uml semant type concept hierarchi produc gain bagofword featur topic model identifi phrase uml use syntax helpdiscuss although topic model outperform graphbas method semant featur deriv uml prove noisi improv perform beyond bagofwordsconclus topic model wsd provid superior result clinic domain howev integr knowledg remain effect exploit

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