title: Robust relational parsing over biomedical literature: extracting inhibit relations.
abstract: We describe the design of a robust parser for identifying and extracting biomolecular relations from the biomedical literature. Separate automata over distinct syntactic domains were developed for extraction of nominal-based relational information versus verbal-based relations. This allowed us to optimize the grammars separately for each module, regardless of any specific relation resulting in significantly better performance. A unique feature of this system is the use of text-based anaphora resolution to enhance the results of argument binding in relational extraction. We demonstrate the performance of our system on inhibition-relations, and present our initial results measured against an annotated text used as a gold standard for evaluation purposes. The results represent a significant improvement over previously published results on extracting such relations from Medline: Precision was 90%, Recall 57%, and Partial Recall 22%. These results demonstrate the effectiveness of a corpus-based linguistic approach to information extraction over Medline.
authors.affiliation: Department of Computer Science, Brandeis University, 415 South St., Waltham, MA 02454, USA. jamesp@cs.brandeis.edu
authors.forename: J
authors.initials: J
authors.lastname: Pustejovsky
authors.forename: J
authors.initials: J
authors.lastname: Castaño
authors.forename: J
authors.initials: J
authors.lastname: Zhang
authors.forename: M
authors.initials: M
authors.lastname: Kotecki
authors.forename: B
authors.initials: B
authors.lastname: Cochran
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D001331:Automation; D019295:Computational Biology / Q000379:methods; D016239:MEDLINE; D015203:Reproducibility of Results; D012107:Research Design; D012680:Sensitivity and Specificity