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Extending and Improving Wordnet via Unsupervised Word Embeddings

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 نشر من قبل Mikhail Khodak
 تاريخ النشر 2017
  مجال البحث الهندسة المعلوماتية
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This work presents an unsupervised approach for improving WordNet that builds upon recent advances in document and sense representation via distributional semantics. We apply our methods to construct Wordnets in French and Russian, languages which both lack good manual constructions.1 These are evaluated on two new 600-word test sets for word-to-synset matching and found to improve greatly upon synset recall, outperforming the best automated Wordnets in F-score. Our methods require very few linguistic resources, thus being applicable for Wordnet construction in low-resources languages, and may further be applied to sense clustering and other Wordnet improvements.



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