J Am Med Inform Assoc - Automatic discourse connective detection in biomedical text.

Tópicos

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Resumo

JECTIVE: Relation extraction in biomedical text mining systems has largely focused on identifying clause-level relations, but increasing sophistication demands the recognition of relations at discourse level. A first step in identifying discourse relations involves the detection of discourse connectives: words or phrases used in text to express discourse relations. In this study supervised machine-learning approaches were developed and evaluated for automatically identifying discourse connectives in biomedical text.MATERIALS AND METHODS: Two supervised machine-learning models (support vector machines and conditional random fields) were explored for identifying discourse connectives in biomedical literature. In-domain supervised machine-learning classifiers were trained on the Biomedical Discourse Relation Bank, an annotated corpus of discourse relations over 24 full-text biomedical articles (~112,000 word tokens), a subset of the GENIA corpus. Novel domain adaptation techniques were also explored to leverage the larger open-domain Penn Discourse Treebank (~1 million word tokens). The models were evaluated using the standard evaluation metrics of precision, recall and F1 scores.RESULTS AND CONCLUSION: Supervised machine-learning approaches can automatically identify discourse connectives in biomedical text, and the novel domain adaptation techniques yielded the best performance: 0.761 F1 score. A demonstration version of the fully implemented classifier BioConn is available at: http://bioconn.askhermes.org.

Resumo Limpo

jectiv relat extract biomed text mine system larg focus identifi clauselevel relat increas sophist demand recognit relat discours level first step identifi discours relat involv detect discours connect word phrase use text express discours relat studi supervis machinelearn approach develop evalu automat identifi discours connect biomed textmateri method two supervis machinelearn model support vector machin condit random field explor identifi discours connect biomed literatur indomain supervis machinelearn classifi train biomed discours relat bank annot corpus discours relat fulltext biomed articl word token subset genia corpus novel domain adapt techniqu also explor leverag larger opendomain penn discours treebank million word token model evalu use standard evalu metric precis recal f scoresresult conclus supervis machinelearn approach can automat identifi discours connect biomed text novel domain adapt techniqu yield best perform f score demonstr version fulli implement classifi bioconn avail httpbioconnaskhermesorg

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