Semantic Role Labeling for Biomedical Corpus Using Maximum Entropy Classifier

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9227)

Abstract

Semantic role labeling (SRL) is a natural language processing (NLP) task that finds shallow semantic representations from sentences. In this paper, we construct a biomedical proposition bank and train a biomedical semantic role labeling system that can be used to facilitate relation extraction and information retrieval in biomedical domain. Firstly, we construct a proposition bank on the basis of the GENIA TreeBank following the Penn PropBank annotation. Secondly, we use GenPropBank to train a biomedical SRL system, which uses maximum entropy as a classifier. Our experimental results show that a newswire SRL system that achieves an F1 of 85.56 % in the newswire domain can only maintain an F1 of 65.43 % when ported to the biomedical domain. By using our annotated biomedical corpus, we can increase that F1 by 19.2 %.

Keywords

Semantic role labeling Dependency parsing Maximum entropy Biomedical corpus 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computer ScienceWuhan UniversityWuhanChina

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