Word embedding based on low-rank doubly stochastic matrix decomposition
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A4 Artikkeli konferenssijulkaisussa
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2018
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Language
en
Pages
10
90-100
90-100
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Neural Information Processing, Volume 3, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 11303 LNCS
Abstract
Word embedding, which encodes words into vectors, is an important starting point in natural language processing and commonly used in many text-based machine learning tasks. However, in most current word embedding approaches, the similarity in embedding space is not optimized in the learning. In this paper we propose a novel neighbor embedding method which directly learns an embedding simplex where the similarities between the mapped words are optimal in terms of minimal discrepancy to the input neighborhoods. Our method is built upon two-step random walks between words via topics and thus able to better reveal the topics among the words. Experiment results indicate that our method, compared with another existing word embedding approach, is more favorable for various queries.Description
Keywords
Nonnegative matrix factorization, Word embedding, Cluster analysis, Doubly stochastic
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Citation
Sedov, D & Yang, Z 2018, Word embedding based on low-rank doubly stochastic matrix decomposition . in Neural Information Processing : 25th International Conference, ICONIP 2018 Siem Reap, Cambodia, December 13–16, 2018 Proceedings, Part III . vol. 3, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11303 LNCS, Springer, pp. 90-100, International Conference on Neural Information Processing, Siem Reap, Cambodia, 13/12/2018 . https://doi.org/10.1007/978-3-030-04182-3_9