Automatic Discourse Connective Detection in Biomedical Text

J Am Med Inform Assoc. Sep-Oct 2012;19(5):800-8. doi: 10.1136/amiajnl-2011-000775. Epub 2012 Jun 28.


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.

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:

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Artificial Intelligence
  • Data Mining / methods*
  • Humans
  • Natural Language Processing*
  • Support Vector Machine