Difference between revisions of "Extracting lexical semantic knowledge from Wikipedia and Wiktionary"

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Revision as of 19:35, December 16, 2013

Publication (help)
Extracting lexical semantic knowledge from Wikipedia and Wiktionary
Authors: Torsten Zesch, Christof Müller, Iryna Gurevych [edit item]
Citation: Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)  : . 2008.
Publication type: Conference paper
Peer-reviewed: Yes
Database(s):
DOI: Define doi.
Google Scholar cites: Citations
Link(s): Paper link
Added by Wikilit team: Added on initial load
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Format: BibTeX
Extracting lexical semantic knowledge from Wikipedia and Wiktionary is a publication by Torsten Zesch, Christof Müller, Iryna Gurevych.


[edit] Abstract

Recently, collaboratively constructed resources such as Wikipedia and Wiktionary have been discovered as valuable lexical semantic knowledge bases with a high potential in diverse Natural Language Processing (NLP) tasks. Collaborative knowledge bases however significantly differ from traditional linguistic knowledge bases in various respects, and this constitutes both an asset and an impediment for research in NLP. This paper addresses one such major impediment, namely the lack of suitable programmatic access mechanisms to the knowledge stored in these large semantic knowledge bases. We present two application programming interfaces for Wikipedia and Wiktionary which are especially designed for mining the rich lexical semantic information dispersed in the knowledge bases, and provide efficient and structured access to the available knowledge. As we believe them to be of general interest to the NLP community, we have made them freely available for research purposes.

[edit] Research questions

"This paper addresses one such major impediment, namely the lack of suitable programmatic access mechanisms to the knowledge stored in these large semantic knowledge bases. We present two application programming interfaces for Wikipedia and Wiktionary which are especially designed for mining the rich lexical semantic information dispersed in the knowledge bases, and provide efficient and structured access to the available knowledge."

Research details

Topics: Computational linguistics [edit item]
Domains: Computer science [edit item]
Theory type: Design and action [edit item]
Wikipedia coverage: Main topic [edit item]
Theories: "Undetermined" [edit item]
Research design: Other [edit item]
Data source: [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: Article [edit item]
Wikipedia data extraction: Clone [edit item]
Wikipedia page type: Article [edit item]
Wikipedia language: English, German [edit item]

[edit] Conclusion

"This paper presented Java based APIs that allow for efficient access to Wikipedia and Wiktionary, and demonstrated cases of their usage. As the APIs are freely available for research purposes, we think that they will foster NLP research using the collaborative knowledge bases Wikipedia and Wiktionary"

[edit] Comments

"Research design: design science"


Further notes[edit]

Facts about "Extracting lexical semantic knowledge from Wikipedia and Wiktionary"RDF feed
AbstractRecently, collaboratively constructed resoRecently, collaboratively constructed resources such as Wikipedia and Wiktionary have been discovered as valuable lexical semantic knowledge bases with a high potential in diverse Natural Language Processing (NLP) tasks. Collaborative knowledge bases however significantly differ from traditional linguistic knowledge bases in various respects, and this constitutes both an asset and an impediment for research in NLP. This paper addresses one such major impediment, namely the lack of suitable programmatic access mechanisms to the knowledge stored in these large semantic knowledge bases. We present two application programming interfaces for Wikipedia and Wiktionary which are especially designed for mining the rich lexical semantic information dispersed in the knowledge bases, and provide efficient and structured access to the available knowledge. As we believe them to be of general interest to the NLP community, we have made them freely available for research purposes.em freely available for research purposes.
Added by wikilit teamAdded on initial load +
Collected data time dimensionCross-sectional +
CommentsResearch design: design science
ConclusionThis paper presented Java based APIs that This paper presented Java based APIs that allow for efficient access to Wikipedia and Wiktionary, and demonstrated cases of their usage. As the APIs are freely available for research purposes, we think that they will foster NLP research using the collaborative knowledge bases Wikipedia and Wiktionarye knowledge bases Wikipedia and Wiktionary
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Extracting%2Blexical%2Bsemantic%2Bknowledge%2Bfrom%2BWikipedia%2Band%2BWiktionary%22 +
Has authorTorsten Zesch +, Christof Müller + and Iryna Gurevych +
Has domainComputer science +
Has topicComputational linguistics +
Peer reviewedYes +
Publication typeConference paper +
Published inProceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08) +
Research designOther +
Research questionsThis paper addresses one such major impediThis paper addresses one such major impediment, namely the lack of suitable programmatic access mechanisms to the knowledge stored in these large semantic knowledge bases. We present two application programming interfaces for Wikipedia and Wiktionary which are especially designed for mining the rich lexical semantic information dispersed in the knowledge bases, and provide efficient and structured access to the available knowledge.uctured access to the available knowledge.
Revid10,171 +
TheoriesUndetermined
Theory typeDesign and action +
TitleExtracting lexical semantic knowledge from Wikipedia and Wiktionary
Unit of analysisArticle +
Urlhttp://hnk.ffzg.hr/bibl/lrec2008/pdf/420_paper.pdf +
Wikipedia coverageMain topic +
Wikipedia data extractionClone +
Wikipedia languageEnglish + and German +
Wikipedia page typeArticle +
Year2008 +