Syntactic dependency parsers for biomedical-NLP

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 Depende...

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Bibliographic Details
Published in:AMIA ... Annual Symposium proceedings 2012, Vol.2012, p.121-128
Main Authors: Cohen, Raphael, Elhadad, Michael
Format: Article
Language:English
Subjects:
Biomedical Research
Information Storage and Retrieval - methods
Medical Records
Natural Language Processing
Online Access:Get full text
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Summary: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.
ISSN:1559-4076