Difference between revisions of "Exploiting Semantic Role Labeling, Wordnet and Wikipedia for Coreference Resolution"
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| − | '''Exploiting Semantic Role Labeling, Wordnet and Wikipedia for Coreference Resolution''' - scientific work related to Wikipedia quality published in 2006, written by Simone Paolo Ponzetto and Michael Strube. | + | '''Exploiting Semantic Role Labeling, Wordnet and Wikipedia for Coreference Resolution''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Simone Paolo Ponzetto]] and [[Michael Strube]]. |
== Overview == | == Overview == | ||
| − | In this paper authors 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. Authors show that semantic features indeed improve the performance on different referring expression types such as pronouns and common nouns. | + | In this paper authors 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. Authors show that semantic features indeed improve the performance on different referring expression types such as pronouns and common nouns. |
Revision as of 09:03, 18 May 2020
Exploiting Semantic Role Labeling, Wordnet and Wikipedia for Coreference Resolution - scientific work related to Wikipedia quality published in 2006, written by Simone Paolo Ponzetto and Michael Strube.
Overview
In this paper authors 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. Authors show that semantic features indeed improve the performance on different referring expression types such as pronouns and common nouns.
