Can word vectors help corpus linguists?

Les vecteurs lexicaux peuvent-ils venir en aide aux linguistes de corpus ?

Guillaume Desagulier (1)

1 MoDyCo - Modèles, Dynamiques, Corpus

Two recent methods based on distributional semantic models (DSMs) have proved very successful in learning high-quality vector representations of words from large corpora: word2vec (Mikolov, Chen, et al. 2013; Mikolov, Yih, et al. 2013) and GloVe (Pennington et al. 2014). Once trained on a very large corpus, these algorithms produce distributed representations for words in the form of vectors. DSMs based on deep learning and neural networks have proved efficient in representing the meaning of individual words. In this paper, I assess to what extent state-of-the-art word-vector semantics can help corpus linguists annotate large datasets for semantic classes. Although word vectors suggest decisive opportunities for resolving semantic annotation issues, it has yet to improve in terms of its representation of polysemy, homonymy, and multiword expressions.

Mots clés: corpus linguistics; word vectors; distributional semantic models; semantic annotation

Domaines: Linguistique; Méthodes et statistiques

Guillaume Desagulier. Can word vectors help corpus linguists?. 2017. ⟨halshs-01657591v1⟩