Correlation-based Intrinsic Evaluation of Word Vector Representations

Yulia Tsvetkov; Manaal Faruqui; Chris Dyer

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arXiv · 1606.06710

Correlation-based Intrinsic Evaluation of Word Vector Representations

Yulia Tsvetkov·Manaal Faruqui·Chris Dyer

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Abstract

We introduce QVEC-CCA--an intrinsic evaluation metric for word vector representations based on correlations of learned vectors with features extracted from linguistic resources. We show that QVEC-CCA scores are an effective proxy for a range of extrinsic semantic and syntactic tasks. We also show that the proposed evaluation obtains higher and more consistent correlations with downstream tasks, compared to existing approaches to intrinsic evaluation of word vectors that are based on word similarity.

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Yulia Tsvetkov, Manaal Faruqui, Chris Dyer. 2016-06-21. Correlation-based Intrinsic Evaluation of Word Vector Representations. https://arxiv.org/abs/1606.06710

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