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        <title>Automatic Enrichment of WordNet with Common-Sense Knowledge</title>
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            <title>Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)</title>
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    <abstract>WordNet represents a cornerstone in the Computational Linguistics field, linking words to meanings (or senses) through a taxonomical representation of synsets, i.e., clusters of words with an equivalent meaning in a specific context often described by few definitions (or glosses) and examples. Most of the approaches to the Word Sense Disambiguation task fully rely on these short texts as a source of contextual information to match with the input text to disambiguate. This paper presents the first attempt to enrich synsets data with common-sense definitions, automatically retrieved from ConceptNet 5, and disambiguated accordingly to WordNet. The aim was to exploit the shared- and immediate-thinking nature of common-sense knowledge to extend the short but incredibly useful contextual information of the synsets. A manual evaluation on a subset of the entire result (which counts a total of almost 600K synset enrichments) shows a very high precision with an estimated good recall.</abstract>
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%0 Conference Proceedings
%T Automatic Enrichment of WordNet with Common-Sense Knowledge
%A Di Caro, Luigi
%A Boella, Guido
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F di-caro-boella-2016-automatic
%X WordNet represents a cornerstone in the Computational Linguistics field, linking words to meanings (or senses) through a taxonomical representation of synsets, i.e., clusters of words with an equivalent meaning in a specific context often described by few definitions (or glosses) and examples. Most of the approaches to the Word Sense Disambiguation task fully rely on these short texts as a source of contextual information to match with the input text to disambiguate. This paper presents the first attempt to enrich synsets data with common-sense definitions, automatically retrieved from ConceptNet 5, and disambiguated accordingly to WordNet. The aim was to exploit the shared- and immediate-thinking nature of common-sense knowledge to extend the short but incredibly useful contextual information of the synsets. A manual evaluation on a subset of the entire result (which counts a total of almost 600K synset enrichments) shows a very high precision with an estimated good recall.
%U https://aclanthology.org/L16-1132
%P 819-822

Markdown (Informal)

[Automatic Enrichment of WordNet with Common-Sense Knowledge](https://aclanthology.org/L16-1132) (Di Caro & Boella, LREC 2016)

• Automatic Enrichment of WordNet with Common-Sense Knowledge (Di Caro & Boella, LREC 2016)

ACL

• Luigi Di Caro and Guido Boella. 2016. Automatic Enrichment of WordNet with Common-Sense Knowledge. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 819–822, Portorož, Slovenia. European Language Resources Association (ELRA).