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PLoS One. 2018 Dec 10;13(12):e0208785. doi: 10.1371/journal.pone.0208785. eCollection 2018.

Improve word embedding using both writing and pronunciation.

Author information

1
School of Computer Engineering and Science, Shanghai University, Shanghai, China.
2
College of Computer Science and Technology, Zhejiang University, Zhejiang, China.
3
Library of Shanghai University, Shanghai University, Shanghai, China.

Abstract

Text representation can map text into a vector space for subsequent use in numerical calculations and processing tasks. Word embedding is an important component of text representation. Most existing word embedding models focus on writing and utilize context, weight, dependency, morphology, etc., to optimize the training. However, from the linguistic point of view, spoken language is a more direct expression of semantics; writing has meaning only as a recording of spoken language. Therefore, this paper proposes the concept of a pronunciation-enhanced word embedding model (PWE) that integrates speech information into training to fully apply the roles of both speech and writing to meaning. This paper uses the Chinese language, English language and Spanish language as examples and presents several models that integrate word pronunciation characteristics into word embedding. Word similarity and text classification experiments show that the PWE outperforms the baseline model that does not include speech information. Language is a storehouse of sound-images; therefore, the PWE can be applied to most languages.

PMID:
30532197
PMCID:
PMC6287836
DOI:
10.1371/journal.pone.0208785
[Indexed for MEDLINE]
Free PMC Article

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