A shallow parser based on closed-class words to capture relations in biomedical text

Gondy Leroy, Hsinchun Chen, Jesse D. Martinez

Research output: Contribution to journalArticlepeer-review

89 Scopus citations


Natural language processing for biomedical text currently focuses mostly on entity and relation extraction. These entities and relations are usually pre-specified entities, e.g., proteins, and pre-specified relations, e.g., inhibit relations. A shallow parser that captures the relations between noun phrases automatically from free text has been developed and evaluated. It uses heuristics and a noun phraser to capture entities of interest in the text. Cascaded finite state automata structure the relations between individual entities. The automata are based on closed-class English words and model generic relations not limited to specific words. The parser also recognizes coordinating conjunctions and captures negation in text, a feature usually ignored by others. Three cancer researchers evaluated 330 relations extracted from 26 abstracts of interest to them. There were 296 relations correctly extracted from the abstracts resulting in 90% precision of the relations and an average of 11 correct relations per abstract.

Original languageEnglish (US)
Pages (from-to)145-158
Number of pages14
JournalJournal of Biomedical Informatics
Issue number3
StatePublished - Jun 2003


  • Biomedicine
  • Bottom-up parser
  • Finite state automata
  • Free text
  • NLP
  • Natural language processing
  • Shallow parsing

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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