title: Automatic discourse connective detection in biomedical text.
abstract: OBJECTIVE
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors: Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53211, USA.
authors: Balaji Polepalli
authors: BP
authors: Ramesh
authors: Rashmi
authors: R
authors: Prasad
authors: Tim
authors: T
authors: Miller
authors: Brian
authors: B
authors: Harrington
authors: Hong
authors: H
authors: Yu
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D006801:Humans; D009323:Natural Language Processing; D060388:Support Vector Machine