AMIA Annu Symp Proc - Identifying discourse connectives in biomedical text.

Tópicos

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Resumo

Discourse connectives are words or phrases that connect or relate two coherent sentences or phrases and indicate the presence of discourse relations. Automatic recognition of discourse connectives may benefit many natural language processing applications. In this pilot study, we report the development of the supervised machine-learning classifiers with conditional random fields (CRFs) for automatically identifying discourse connectives in full-text biomedical articles. Our first classifier was trained on the open-domain 1 million token Penn Discourse Tree Bank (PDTB). We performed cross validation on biomedical articles (approximately 100K word tokens) that we annotated. The results show that the classifier trained on PDTB data attained a 0.55 F1-score for identifying discourse connectives in biomedical text, while the cross-validation results in the biomedical text attained a 0.69 F1-score, a much better performance despite a much smaller training size. Our preliminary analysis suggests the existence of domain-specific features, and we speculate that domain-adaption approaches may further improve performance.

Resumo Limpo

discours connect word phrase connect relat two coher sentenc phrase indic presenc discours relat automat recognit discours connect may benefit mani natur languag process applic pilot studi report develop supervis machinelearn classifi condit random field crfs automat identifi discours connect fulltext biomed articl first classifi train opendomain million token penn discours tree bank pdtb perform cross valid biomed articl approxim k word token annot result show classifi train pdtb data attain fscore identifi discours connect biomed text crossvalid result biomed text attain fscore much better perform despit much smaller train size preliminari analysi suggest exist domainspecif featur specul domainadapt approach may improv perform

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