ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2010
Identifying discourse connectives in biomedical text.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Detecting causality from online psychiatric texts using inter-sentential language patterns.BMC medical informatics and decision making · 2012Article
- The biomedical discourse relation bank.BMC bioinformatics · 2011Article
- Automatic discourse connective detection in biomedical text.Journal of the American Medical Informatics Association : JAMIAArticle
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Authors and funding
2 authors.
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Abstract
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.
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21347060PMC3041460What OpenQuestion holds
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