title: Syntactic dependency parsers for biomedical-NLP.
abstract: Syntactic parsers have made a leap in accuracy and speed in recent years. The high order structural information provided by dependency parsers is useful for a variety of NLP applications. We present a biomedical model for the EasyFirst parser, a fast and accurate parser for creating Stanford Dependencies. We evaluate the models trained in the biomedical domains of EasyFirst and Clear-Parser in a number of task oriented metrics. Both parsers provide stat of the art speed and accuracy in the Genia of over 89%. We show that Clear-Parser excels at tasks relating to negation identification while EasyFirst excels at tasks relating to Named Entities and is more robust to changes in domain.
authors.affiliation: Computer Science Dept, Ben-Gurion University of the Negev, Israel. cohenrap@bgu.ac.il
authors.forename: Raphael
authors.initials: R
authors.lastname: Cohen
authors.forename: Michael
authors.initials: M
authors.lastname: Elhadad
mesh_terms: D035843:Biomedical Research; D016247:Information Storage and Retrieval / Q000379:methods*; D008499:Medical Records; D009323:Natural Language Processing