Beyond word2vec: Distance-graph Tensor Factorization for Word and Document Embeddings

Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1041-1050, 2019.

Cited by: 1|Bibtex|Views46|DOI:https://doi.org/10.1145/3357384.3358051
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Other Links: dl.acm.org|dblp.uni-trier.de|academic.microsoft.com

Abstract:

The \em word2vec methodology such as Skip-gram and CBOW has seen significant interest in recent years because of its ability to model semantic notions of word similarity and distances in sentences. A related methodology, referred to as \em doc2vec is also able to embed sentences and paragraphs. These methodologies, however, lead to differ...More

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