Using Wiktionary for computing semantic relatedness

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Using Wiktionary for computing semantic relatedness
Authors: Torsten Zesch, Christof Müller, Iryna Gurevych [edit item]
Citation: AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2  : 861-866. 2008.
Publication type: Conference paper
Peer-reviewed: Yes
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Using Wiktionary for computing semantic relatedness is a publication by Torsten Zesch, Christof Müller, Iryna Gurevych.


[edit] Abstract

We introduce Wiktionary as an emerging lexical semantic resource that can be used as a substitute for expert-made resources in {AI} applications. We evaluate Wiktionary on the pervasive task of computing semantic relatedness for English and German by means of correlation with human rankings and solving word choice problems. For the first time, we apply a concept vector based measure to a set of different concept representations like Wiktionary pseudo glosses, the first paragraph of Wikipedia articles, English {WordNet} glosses, and {GermaNet} pseudo glosses. We show that: (i) Wiktionary is the best lexical semantic resource in the ranking task and performs comparably to other resources in the word choice task, and (ii) the concept vector based approach yields the best results on all datasets in both evaluations.

[edit] Research questions

"We introduce Wiktionary as an emerging lexical semantic resource that can be used as a substitute for expert-made resources in AI applications. We evaluate Wiktionary on the pervasive task of computing semantic relatedness for English and German by means of correlation with human rankings and solving word choice problems."

Research details

Topics: Semantic relatedness [edit item]
Domains: Computer science [edit item]
Theory type: Design and action [edit item]
Wikipedia coverage: Sample data [edit item]
Theories: "Undetermined" [edit item]
Research design: Experiment [edit item]
Data source: [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: Article [edit item]
Wikipedia data extraction: Dump [edit item]
Wikipedia page type: Article [edit item]
Wikipedia language: English, German [edit item]

[edit] Conclusion

"We show that: (i) Wiktionary is the best lexical semantic resource in the ranking task and performs comparably to other resources in the word choice task, and (ii) the concept vector based approach yields the best results on all datasets in both evaluations"

[edit] Comments

""We show that: (i) Wiktionary is the best lexical semantic resource in the ranking task and performs comparably to other resources in the word choice task, and (ii) the concept vector based approach yields the best results on all datasets in both evaluations" p. 861"


Further notes[edit]

Facts about "Using Wiktionary for computing semantic relatedness"RDF feed
AbstractWe introduce Wiktionary as an emerging lexWe introduce Wiktionary as an emerging lexical semantic resource that can be used as a substitute for expert-made resources in {AI} applications. We evaluate Wiktionary on the pervasive task of computing semantic relatedness for English and German by means of correlation with human rankings and solving word choice problems. For the first time, we apply a concept vector based measure to a set of different concept representations like Wiktionary pseudo glosses, the first paragraph of Wikipedia articles, English {WordNet} glosses, and {GermaNet} pseudo glosses. We show that: (i) Wiktionary is the best lexical semantic resource in the ranking task and performs comparably to other resources in the word choice task, and (ii) the concept vector based approach yields the best results on all datasets in both evaluations.sults on all datasets in both evaluations.
Added by wikilit teamAdded on initial load +
Collected data time dimensionCross-sectional +
Comments"We show that: (i) Wiktionary is the best "We show that: (i) Wiktionary is the best lexical semantic resource in the ranking task and performs comparably to other resources in the word choice task, and (ii) the concept vector based approach yields the best results on all datasets in both evaluations" p. 861n all datasets in both evaluations" p. 861
ConclusionWe show that: (i) Wiktionary is the best lWe show that: (i) Wiktionary is the best lexical semantic resource in the ranking task and performs comparably to other resources in the word choice task, and (ii) the concept vector based approach yields the best results on all datasets in both evaluationsesults on all datasets in both evaluations
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Using%2BWiktionary%2Bfor%2Bcomputing%2Bsemantic%2Brelatedness%22 +
Has authorTorsten Zesch +, Christof Müller + and Iryna Gurevych +
Has domainComputer science +
Has topicSemantic relatedness +
Pages861-866 +
Peer reviewedYes +
Publication typeConference paper +
Published inAAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 +
Research designExperiment +
Research questionsWe introduce Wiktionary as an emerging lexWe introduce Wiktionary as an emerging lexical semantic resource that can be used as a substitute for expert-made resources in AI applications. We evaluate Wiktionary on the pervasive task of computing semantic relatedness for English and German by means of correlation with human rankings and solving word choice problems.rankings and solving word choice problems.
Revid10,508 +
TheoriesUndetermined
Theory typeDesign and action +
TitleUsing Wiktionary for computing semantic relatedness
Unit of analysisArticle +
Urlhttp://en.scientificcommons.org/45658519 +
Wikipedia coverageSample data +
Wikipedia data extractionDump +
Wikipedia languageEnglish + and German +
Wikipedia page typeArticle +
Year2008 +