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Semantic role labeling for protein transport predicates

Journal Article · · BMC Bioinformatics
 [1];  [2];  [3];  [4]
  1. Univ. of Colorado, Boulder, CO (United States). Computer Science Dept.; DOE/OSTI
  2. National Library of Medicine (NLM), Bethesda, MD (United States). National Center for Biotechnology Information
  3. Univ. of Colorado, Boulder, CO (United States). Computer Science Dept.
  4. Univ. of Colorado, Aurora, CO (United States). School of Medicine. Center for Computational Pharmacology
+ Show Author Affiliations

Background: Automatic semantic role labeling (SRL) is a natural language processing (NLP) technique that maps sentences to semantic representations. This technique has been widely studied in the recent years, but mostly with data in newswire domains. Here, we report on a SRL model for identifying the semantic roles of biomedical predicates describing protein transport in GeneRIFs – manually curated sentences focusing on gene functions. To avoid the computational cost of syntactic parsing, and because the boundaries of our protein transport roles often did not match up with syntactic phrase boundaries, we approached this problem with a word-chunking paradigm and trained support vector machine classifiers to classify words as being at the beginning, inside or outside of a protein transport role. Results: We collected a set of 837 GeneRIFs describing movements of proteins between cellular components, whose predicates were annotated for the semantic roles AGENT, PATIENT, ORIGIN and DESTINATION. We trained these models with the features of previous wordchunking models, features adapted from phrase-chunking models, and features derived from an analysis of our data. Our models were able to label protein transport semantic roles with 87.6% precision and 79.0% recall when using manually annotated protein boundaries, and 87.0% precision and 74.5% recall when using automatically identified ones. Conclusion: We successfully adapted the word-chunking classification paradigm to semantic role labeling, applying it to a new domain with predicates completely absent from any previous studies. By combining the traditional word and phrasal role labeling features with biomedical features like protein boundaries and MEDPOST part of speech tags, we were able to address the challenges posed by the new domain data and subsequently build robust models that achieved F-measures as high as 83.1. This system for extracting protein transport information from GeneRIFs performs well even with proteins identified automatically, and is therefore more robust than the rule-based methods previously used to extract protein transport roles.

Research Organization:
Oak Ridge Associated Univ., Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
Grant/Contract Number:
AC05-06OR23100
OSTI ID:
1626355
Journal Information:
BMC Bioinformatics, Journal Name: BMC Bioinformatics Journal Issue: 1 Vol. 9; ISSN 1471-2105
Publisher:
BioMed CentralCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (2)

A resource-saving collective approach to biomedical semantic role labeling journal May 2014
Large Scale Application of Neural Network Based Semantic Role Labeling for Automated Relation Extraction from Biomedical Texts journal July 2009

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