Non-distributional Word Vector Representations

Manaal Faruqui; Chris Dyer

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arXiv · 1506.05230

Non-distributional Word Vector Representations

Manaal Faruqui·Chris Dyer

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Abstract

Data-driven representation learning for words is a technique of central importance in NLP. While indisputably useful as a source of features in downstream tasks, such vectors tend to consist of uninterpretable components whose relationship to the categories of traditional lexical semantic theories is tenuous at best. We present a method for constructing interpretable word vectors from hand-crafted linguistic resources like WordNet, FrameNet etc. These vectors are binary (i.e, contain only 0 and 1) and are 99.9% sparse. We analyze their performance on state-of-the-art evaluation methods for distributional models of word vectors and find they are competitive to standard distributional approaches.

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Manaal Faruqui, Chris Dyer. 2015-06-17. Non-distributional Word Vector Representations. https://arxiv.org/abs/1506.05230

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