@inproceedings{camacho-collados-navigli-2017-babeldomains,
title = "{B}abel{D}omains: Large-Scale Domain Labeling of Lexical Resources",
author = "Camacho-Collados, Jose and
Navigli, Roberto",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-2036",
pages = "223--228",
abstract = "In this paper we present BabelDomains, a unified resource which provides lexical items with information about domains of knowledge. We propose an automatic method that uses knowledge from various lexical resources, exploiting both distributional and graph-based clues, to accurately propagate domain information. We evaluate our methodology intrinsically on two lexical resources (WordNet and BabelNet), achieving a precision over 80{\%} in both cases. Finally, we show the potential of BabelDomains in a supervised learning setting, clustering training data by domain for hypernym discovery.",
}
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%0 Conference Proceedings
%T BabelDomains: Large-Scale Domain Labeling of Lexical Resources
%A Camacho-Collados, Jose
%A Navigli, Roberto
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F camacho-collados-navigli-2017-babeldomains
%X In this paper we present BabelDomains, a unified resource which provides lexical items with information about domains of knowledge. We propose an automatic method that uses knowledge from various lexical resources, exploiting both distributional and graph-based clues, to accurately propagate domain information. We evaluate our methodology intrinsically on two lexical resources (WordNet and BabelNet), achieving a precision over 80% in both cases. Finally, we show the potential of BabelDomains in a supervised learning setting, clustering training data by domain for hypernym discovery.
%U https://aclanthology.org/E17-2036
%P 223-228
Markdown (Informal)
[BabelDomains: Large-Scale Domain Labeling of Lexical Resources](https://aclanthology.org/E17-2036) (Camacho-Collados & Navigli, EACL 2017)
ACL
- Jose Camacho-Collados and Roberto Navigli. 2017. BabelDomains: Large-Scale Domain Labeling of Lexical Resources. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 223–228, Valencia, Spain. Association for Computational Linguistics.