Scoring Lexical Entailment with a Supervised Directional Similarity Network

Marek Rei; Daniela Gerz; Ivan Vulić

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arXiv · 1805.09355

Scoring Lexical Entailment with a Supervised Directional Similarity Network

Marek Rei·Daniela Gerz·Ivan Vulić

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Abstract

We present the Supervised Directional Similarity Network (SDSN), a novel neural architecture for learning task-specific transformation functions on top of general-purpose word embeddings. Relying on only a limited amount of supervision from task-specific scores on a subset of the vocabulary, our architecture is able to generalise and transform a general-purpose distributional vector space to model the relation of lexical entailment. Experiments show excellent performance on scoring graded lexical entailment, raising the state-of-the-art on the HyperLex dataset by approximately 25%.

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Marek Rei, Daniela Gerz, Ivan Vulić. 2018-05-23. Scoring Lexical Entailment with a Supervised Directional Similarity Network. https://arxiv.org/abs/1805.09355

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