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        <title>Multilingual Wordnet sense Ranking using nearest context</title>
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        <namePart type="given">Umamaheswari</namePart>
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    <abstract>In this paper, we combine methods to estimate sense rankings from raw text with recent work on word embeddings to provide sense ranking estimates for the entries in the Open Multilingual WordNet (OMW). The existing Word2Vec pre-trained models from Polygot2 are only built for single word entries, we, therefore, re-train them with multiword expressions from the wordnets, so that multiword expressions can also be ranked. Thus this trained model gives embeddings for both single words and multiwords. The resulting lexicon gives a WSD baseline for five languages. The results are evaluated for Semcor sense corpora for 5 languages using Word2Vec and Glove models. The Glove model achieves an average accuracy of 0.47 and Word2Vec achieves 0.31 for languages such as English, Italian, Indonesian, Chinese and Japanese. The experimentation on OMW sense ranking proves that the rank correlation is generally similar to the human ranking. Hence distributional semantics can aid in Wordnet Sense Ranking.</abstract>
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%0 Conference Proceedings
%T Multilingual Wordnet sense Ranking using nearest context
%A Vasanthakumar, E. Umamaheswari
%A Bond, Francis
%Y Bond, Francis
%Y Vossen, Piek
%Y Fellbaum, Christiane
%S Proceedings of the 9th Global Wordnet Conference
%D 2018
%8 January
%I Global Wordnet Association
%C Nanyang Technological University (NTU), Singapore
%F vasanthakumar-bond-2018-multilingual
%X In this paper, we combine methods to estimate sense rankings from raw text with recent work on word embeddings to provide sense ranking estimates for the entries in the Open Multilingual WordNet (OMW). The existing Word2Vec pre-trained models from Polygot2 are only built for single word entries, we, therefore, re-train them with multiword expressions from the wordnets, so that multiword expressions can also be ranked. Thus this trained model gives embeddings for both single words and multiwords. The resulting lexicon gives a WSD baseline for five languages. The results are evaluated for Semcor sense corpora for 5 languages using Word2Vec and Glove models. The Glove model achieves an average accuracy of 0.47 and Word2Vec achieves 0.31 for languages such as English, Italian, Indonesian, Chinese and Japanese. The experimentation on OMW sense ranking proves that the rank correlation is generally similar to the human ranking. Hence distributional semantics can aid in Wordnet Sense Ranking.
%U https://aclanthology.org/2018.gwc-1.32
%P 272-283

Markdown (Informal)

[Multilingual Wordnet sense Ranking using nearest context](https://aclanthology.org/2018.gwc-1.32) (Vasanthakumar & Bond, GWC 2018)

• Multilingual Wordnet sense Ranking using nearest context (Vasanthakumar & Bond, GWC 2018)

ACL

• E Umamaheswari Vasanthakumar and Francis Bond. 2018. Multilingual Wordnet sense Ranking using nearest context. In Proceedings of the 9th Global Wordnet Conference, pages 272–283, Nanyang Technological University (NTU), Singapore. Global Wordnet Association.