Automatic retrieval and clustering of similar words
D. Lin.
Proceedings of the 17th international conference on Computational linguistics, page 768--774. Morristown, NJ, USA, Association for Computational Linguistics, (1998)

Bootstrapping semantics from text is one of the greatest challenges in natural language learning. We first define a word similarity measure based on the distributional pattern of words. The similarity measure allows us to construct a thesaurus using a parsed corpus. We then present a new evaluation methodology for the automatically constructed thesaurus. The evaluation results show that the thesaurus is significantly closer to WordNet than Roget Thesaurus is.

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@inproceedings{lin1998automatic,
  abstract = {Bootstrapping semantics from text is one of the greatest challenges in natural language learning. We first define a word similarity measure based on the distributional pattern of words. The similarity measure allows us to construct a thesaurus using a parsed corpus. We then present a new evaluation methodology for the automatically constructed thesaurus. The evaluation results show that the thesaurus is significantly closer to WordNet than Roget Thesaurus is.},
  added-at = {2011-09-13T11:53:46.000+0200},
  address = {Morristown, NJ, USA},
  author = {Lin, Dekang},
  biburl = {https://puma.uni-kassel.de/bibtex/23b08719db9a27c9e8fa0d5d8f5c19a10/benz},
  booktitle = {Proceedings of the 17th international conference on Computational linguistics},
  description = {Automatic retrieval and clustering of similar words},
  doi = {http://dx.doi.org/10.3115/980691.980696},
  interhash = {686133b216cc5960608af406a8f52287},
  intrahash = {3b08719db9a27c9e8fa0d5d8f5c19a10},
  keywords = {lexical_relatedness text},
  location = {Montreal, Quebec, Canada},
  pages = {768--774},
  publisher = {Association for Computational Linguistics},
  timestamp = {2011-09-13T11:53:57.000+0200},
  title = {Automatic retrieval and clustering of similar words},
  url = {http://portal.acm.org/citation.cfm?id=980696},
  year = 1998
}

%0 Conference Paper
%1 lin1998automatic
%A Lin, Dekang
%B Proceedings of the 17th international conference on Computational linguistics
%C Morristown, NJ, USA
%D 1998
%I Association for Computational Linguistics
%K lexical_relatedness text
%P 768--774
%R http://dx.doi.org/10.3115/980691.980696
%T Automatic retrieval and clustering of similar words
%U http://portal.acm.org/citation.cfm?id=980696
%X Bootstrapping semantics from text is one of the greatest challenges in natural language learning. We first define a word similarity measure based on the distributional pattern of words. The similarity measure allows us to construct a thesaurus using a parsed corpus. We then present a new evaluation methodology for the automatically constructed thesaurus. The evaluation results show that the thesaurus is significantly closer to WordNet than Roget Thesaurus is.

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Automatic retrieval and clustering of similar words
D. Lin.
Proceedings of the 17th international conference on Computational linguistics, page 768--774. Morristown, NJ, USA, Association for Computational Linguistics, (1998)

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PUMA

Automatic retrieval and clustering of similar wordsD. Lin. Proceedings of the 17th international conference on Computational linguistics, page 768--774. Morristown, NJ, USA, Association for Computational Linguistics, (1998)

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Cite this publication

Automatic retrieval and clustering of similar words
D. Lin.
Proceedings of the 17th international conference on Computational linguistics, page 768--774. Morristown, NJ, USA, Association for Computational Linguistics, (1998)