Semantic Role Labeling Using Lexical Statistical Information

Semantic Role Labeling Using Lexical Statistical Information. Ponzetto, S., P. & Strube, M. In Proceedings of the Ninth Conference on Computational Natural Language Learning CoNLL2005, pages 213-216, 2005. Association for Computational Linguistics.

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Our system for semantic role labeling is multi-stage in nature, being based on tree pruning techniques, statistical methods for lexicalised feature encoding, and a C4.5 decision tree classiﬁer. We use both shal- low and deep syntactic information from automatically generated chunks and parse trees, and develop a model for learning the semantic arguments of predicates as a multi-class decision problem. We evalu- ate the performance on a set of relatively ‘cheap’ features and report an F1 score of 68.13% on the overall test set.

@inProceedings{
 title = {Semantic Role Labeling Using Lexical Statistical Information},
 type = {inProceedings},
 year = {2005},
 pages = {213-216},
 issue = {June},
 websites = {http://www.aclweb.org/anthology/W/W05/W05-0633},
 publisher = {Association for Computational Linguistics},
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 created = {2012-02-28T00:51:15.000Z},
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 abstract = {Our system for semantic role labeling is multi-stage in nature, being based on tree pruning techniques, statistical methods for lexicalised feature encoding, and a C4.5 decision tree classiﬁer. We use both shal- low and deep syntactic information from automatically generated chunks and parse trees, and develop a model for learning the semantic arguments of predicates as a multi-class decision problem. We evalu- ate the performance on a set of relatively ‘cheap’ features and report an F1 score of 68.13% on the overall test set.},
 bibtype = {inProceedings},
 author = {Ponzetto, Simone Paolo and Strube, Michael},
 booktitle = {Proceedings of the Ninth Conference on Computational Natural Language Learning CoNLL2005}
}

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