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CCF International Conference on Natural Language Processing and Chinese Computing

NLPCC 2015: Natural Language Processing and Chinese Computing pp 380–387Cite as

Bilingual Lexicon Extraction with Temporal Distributed Word Representation from Comparable Corpora

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9362))

Abstract

Distributed word representation has been found to be highly effective to extract a bilingual lexicon from comparable corpora by a simple linear transformation. However, polysemous words often vary their meanings at different time points in the corresponding corpora. A single word representation which is learned from the whole corpora can’t express the temporal change of the word meaning very well. This paper proposes a simple solution which exploits the temporal distributed word representation for polysemous words. The experimental results confirm that the proposed solution can offer better performance on the English-to-Chinese bilingual lexicon extraction task.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Chunyue Zhang & Tiejun Zhao

Authors
  1. Chunyue Zhang
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  2. Tiejun Zhao
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Correspondence to Tiejun Zhao .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Juanzi Li

  2. Rensselaer Polytechnic Institute, Troy, NY, USA

    Heng Ji

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Peking University, Beijing, China

    Yansong Feng

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© 2015 Springer International Publishing Switzerland

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Zhang, C., Zhao, T. (2015). Bilingual Lexicon Extraction with Temporal Distributed Word Representation from Comparable Corpora. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science(), vol 9362. Springer, Cham. https://doi.org/10.1007/978-3-319-25207-0_33

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  • DOI: https://doi.org/10.1007/978-3-319-25207-0_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25206-3

  • Online ISBN: 978-3-319-25207-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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