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NLPCC 2016 Shared Task Chinese Words Similarity Measure via Ensemble Learning Based on Multiple Resources

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Natural Language Understanding and Intelligent Applications (ICCPOL 2016, NLPCC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10102))

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Abstract

Many Chinese words similarity measure algorithms have been introduced since it’s a fundamental issue in various tasks of natural language processing. Previous work focused mainly on using existing semantic knowledge bases or large-scale corpora. However, knowledge base and corpus have limitations for broad coverage and data update. Thus, ensemble learning is then used to improve performance by combing similarities. This paper describes a Chinese word similarity measure using ensemble learning of knowledge and corpus-based algorithms. To be specific, knowledge-based methods are based on TYCCL and Hownet. Two corpus-based methods compute similarities via retrieving on web search engines and deep learning on large-scale corpora (news and microblog). All similarities are combined through support vector regression to get final similarity. Evaluation suggests that TYCCL-based method behaves best according to testing dataset. However, if tuning parameters appropriately, ensemble learning could outperform all the other algorithms. Besides, deep learning on news corpora is better than other corpus-based methods.

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Notes

  1. 1.

    Available at: https://code.google.com/p/word2vec/.

  2. 2.

    Available at: http://ictclas.nlpir.org/.

  3. 3.

    Available at: http://radimrehurek.com/gensim/index.html.

  4. 4.

    Available at: https://www.csie.ntu.edu.tw/~cjlin/libsvm/.

  5. 5.

    Available at: http://www.keenage.com/.

  6. 6.

    Available at: http://pennyliang.com/.

  7. 7.

    Available at: http://lafnews.com/corpus/.

  8. 8.

    20 websites are selected based on the URL amount except two video websites.

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Acknowledgments

This work is supported by Major Projects of National Social Science Fund (13&ZD174), National Social Science Fund Project (No. 14BTQ033) and the Graduate Students Education Innovation Project of Jiangsu Province (No. KYLX16_0407).

Author information

Authors and Affiliations

  1. Department of Information Management, Nanjing University of Science and Technology, Nanjing, 210094, China

    Shutian Ma, Xiaoyong Zhang & Chengzhi Zhang

  2. Jiangsu Key Laboratory of Data Engineering and Knowledge Service (Nanjing University), Nanjing, 210093, China

    Chengzhi Zhang

Authors
  1. Shutian Ma
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  2. Xiaoyong Zhang
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  3. Chengzhi Zhang
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Corresponding author

Correspondence to Chengzhi Zhang .

Editor information

Editors and Affiliations

  1. Microsoft Research Asia, Beijing, China

    Chin-Yew Lin

  2. Brandeis University, Waltham, Massachusetts, USA

    Nianwen Xue

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Fudan University, Shanghai, China

    Xuanjing Huang

  5. Peking University, Beijing, China

    Yansong Feng

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Ma, S., Zhang, X., Zhang, C. (2016). NLPCC 2016 Shared Task Chinese Words Similarity Measure via Ensemble Learning Based on Multiple Resources. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_79

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  • DOI: https://doi.org/10.1007/978-3-319-50496-4_79

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  • Print ISBN: 978-3-319-50495-7

  • Online ISBN: 978-3-319-50496-4

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