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Rotations and Interpretability of Word Embeddings: The Case of the Russian Language

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Analysis of Images, Social Networks and Texts (AIST 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10716))


Consider a continuous word embedding model. Usually, the cosines between word vectors are used as a measure of similarity of words. These cosines do not change under orthogonal transformations of the embedding space. We demonstrate that, using some canonical orthogonal transformations from SVD, it is possible both to increase the meaning of some components and to make the components more stable under re-learning. We study the interpretability of components for publicly available models for the Russian language (RusVectōrēs, fastText, RDT).

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    With numpy.linalg.svd it took up to several minutes for 100 K vocabulary.

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The author is grateful to Mikhail Dektyarev, Mikhail Nokel, Anna Potapenko and Daniil Tararukhin for valuable and fruitful discussions.

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Correspondence to Alexey Zobnin .

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Top/bottom words for the first few principal components for different Russian models

Table 1. RDT model, dim = 100, 10 K most frequent words
Table 2. fastText model, 100 K most frequent words
Table 3. RusVectōrēs web model, 100 K most frequent words

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Zobnin, A. (2018). Rotations and Interpretability of Word Embeddings: The Case of the Russian Language. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2017. Lecture Notes in Computer Science(), vol 10716. Springer, Cham.

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