Turning Distributional Thesauri into Word Vectors for Synonym Extraction and Expansion
Résumé
In this article, we propose to investigate
a new problem consisting in turning a
distributional thesaurus into dense word
vectors. We propose more precisely a
method for performing such task by associating
graph embedding and distributed
representation adaptation. We have applied
and evaluated it for English nouns
at a large scale about its ability to retrieve
synonyms. In this context, we have
also illustrated the interest of the developed
method for three different tasks: the
improvement of already existing word embeddings,
the fusion of heterogeneous representations
and the expansion of synsets
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