Order Embeddings for Supervised Hypernymy Detection

Mathias Etcheverry, Dina Wonsever


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.


Hypernymy, word embedding, order embedding, neural network, siamese network

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