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Exploiting semantic role labeling, WordNet and Wikipedia for coreference resolution
Abstract In this paper we present an extension of aIn this paper we present an extension of a machine learning based coreference resolution system which uses features induced from different semantic knowledge sources. These features represent knowledge mined from {WordNet} and Wikipedia, as well as information about semantic role labels. We show that semantic features indeed improve the performance on different referring expression types such as pronouns and common nouns.n types such as pronouns and common nouns.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Conclusion Empirical results show that coreference reEmpirical results show that coreference resolution benefits from semantics. The generated model is able to learn selectional preferences in cases where surface morpho-syntactic features do not suffice, i.e. pronoun and common name resolution. While the results given by using ‘the free encyclopedia that anyone can edit’ are satisfactory, major improvements can come from developing efficient query strategies – i.e. a more refined disambiguation technique taking advantage of the context in which the queries (e.g. referring expressions) occur.ueries (e.g. referring expressions) occur.
Data source Experiment responses  + , Websites  + , Wikipedia pages  +
Doi 10.3115/1220835.1220860 +
Google scholar url  +
Has author Simone Paolo Ponzetto + , Michael Strube +
Has domain Computer science +
Has topic Other natural language processing topics +
Pages 192-199  +
Peer reviewed Yes  +
Publication type Conference paper  +
Published in HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics +
Research design Experiment  +
Research questions This paper explores whether coreference reThis paper explores whether coreference resolution can benefit from semantic knowledge sources. More specifically, whether a machine learning based approach to coreference resolution can be improved and which phenomena are affected by such information. We investigate the use of the WordNet and Wikipedia taxonomies for extracting semantic similarity and relatedness measures, as well as semantic parsing information in terms of semantic role labeling (Gildea & Jurafsky, 2002, SRL henceforth).dea & Jurafsky, 2002, SRL henceforth).
Revid 10,761  +
Theories Undetermined
Theory type Design and action  +
Title Exploiting semantic role labeling, WordNet and Wikipedia for coreference resolution
Unit of analysis Article  +
Url  +
Wikipedia coverage Sample data  +
Wikipedia data extraction Dump  +
Wikipedia language English  +
Wikipedia page type Article  +
Year 2006  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:27:54  +
Categories Other natural language processing topics  + , Computer science  + , Publications with missing comments  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:26:13  +
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