This paper presents a comparative evaluation of several state-of-the-art English parsers based on different frameworks. Our approach is to measure the impact of each parser when it is used as a component of an information extraction system that performs protein-protein interaction (PPI) identification in biomedical papers. We evaluate eight parsers (based on dependency parsing, phrase structure parsing, or deep parsing) using five different parse representations. We run a PPI system with several combinations of parser and parse representation, and examine their impact on PPI identification accuracy. Our experiments show that the levels of accuracy obtained with these different parsers are similar, but that accuracy improvements vary when the parsers are retrained with domain-specific data.
REF compared parser quality in the biomedical domain using a protein-protein interaction (PPI) identification accuracy metric.
Task-oriented Evaluation of Syntactic Parsers and Their Representations
Annual Meeting Of The Association For Computational Linguistics
Computer Science