Complex networks based word embeddings

Nicolas Dugué; Victor Connes

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arXiv · 1910.01489

Complex networks based word embeddings

Nicolas Dugué·Victor Connes

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Abstract

Most of the time, the first step to learn word embeddings is to build a word co-occurrence matrix. As such matrices are equivalent to graphs, complex networks theory can naturally be used to deal with such data. In this paper, we consider applying community detection, a main tool of this field, to the co-occurrence matrix corresponding to a huge corpus. Community structure is used as a way to reduce the dimensionality of the initial space. Using this community structure, we propose a method to extract word embeddings that are comparable to the state-of-the-art approaches.

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BibTeX RIS

Nicolas Dugué, Victor Connes. 2019-10-03. Complex networks based word embeddings. https://arxiv.org/abs/1910.01489

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