Dataset Information

Automatic discourse connective detection in biomedical text.

ABSTRACT: 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.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.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.

SUBMITTER: Ramesh BP

PROVIDER: S-EPMC3422833 | biostudies-literature | 2012 Sep-Oct

REPOSITORIES: biostudies-literature

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Automatic discourse connective detection in biomedical text.

Ramesh Balaji Polepalli BP Prasad Rashmi R Miller Tim T Harrington Brian B Yu Hong H

Journal of the American Medical Informatics Association : JAMIA 20120628 5

<h4>Objective</h4>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 c ...[more]

PMID: 22744958

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Dataset Information

Automatic discourse connective detection in biomedical text.

Publications

Automatic discourse connective detection in biomedical text.

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