Intrinsic Subspace Evaluation of Word Embedding Representations

Yadollah Yaghoobzadeh; Hinrich Schütze

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arXiv · 1606.07902

Intrinsic Subspace Evaluation of Word Embedding Representations

Yadollah Yaghoobzadeh·Hinrich Schütze

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Abstract

We introduce a new methodology for intrinsic evaluation of word representations. Specifically, we identify four fundamental criteria based on the characteristics of natural language that pose difficulties to NLP systems; and develop tests that directly show whether or not representations contain the subspaces necessary to satisfy these criteria. Current intrinsic evaluations are mostly based on the overall similarity or full-space similarity of words and thus view vector representations as points. We show the limits of these point-based intrinsic evaluations. We apply our evaluation methodology to the comparison of a count vector model and several neural network models and demonstrate important properties of these models.

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BibTeX RIS

Yadollah Yaghoobzadeh, Hinrich Schütze. 2016-06-25. Intrinsic Subspace Evaluation of Word Embedding Representations. https://arxiv.org/abs/1606.07902

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