Resumo

ETCHEVERRY, Mathias  e  WONSEVER, Dina. Order Embeddings for Supervised Hypernymy Detection. Comp. y Sist. [online]. 2020, vol.24, n.2, pp.565-574.  Epub 04-Out-2021. ISSN 2007-9737.  https://doi.org/10.13053/cys-24-2-3390.

In this work we present a supervised approach to partially order word embeddings, through a learned order embedding, and we apply it in supervised hypernymy detection. We use neural network as an order embedding to map general purpose word embeddings to a partially ordered vector set. The mapping is trained using positive and negative instances of the relationship. We consider two alternatives to deal with compound terms: a character based embedding of an underscored version of the terms, and a convolutional neural network that consumes the word embedding of each term. We show that this distributional approach presents interesting results in comparison to other distributional and path-based approaches. In addition, we observe still good behavior on different sized portions of the training data. This may suggest an interesting generalization capability.

Palavras-chave : Hypernymy; word embedding; order embedding; neural network; Siamese network.

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