skip to main content
10.1145/3319921.3319938acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiciaiConference Proceedingsconference-collections
research-article

Semantic Relationship between Abbreviations and the Original Words Based on Word Vectors

Published: 15 March 2019 Publication History

Abstract

Based on word vectors, this paper studies the semantic relationship between abbreviations and the original words from a quantitative perspective. First, we train word vectors according to the corpus of the People's Daily. Then, from the perspectives of intrinsic evaluation and extrinsic evaluation, the research on the relationship between abbreviations and the original words is carried out. In the intrinsic evaluation, after vectorizing the abbreviations and their original words, we measure their semantic similarity, and compare with the semantic similarity of synonym collection; In the extrinsic evaluation, we propose an abbreviation recognition task, like named entity recognition. Through this task, the degree of mutual substitution between abbreviations and the original words can be described. The above experiments show that after the abbreviations and their original words are expressed by word vectors, there is still a strong semantic relationship between them.

References

[1]
Zi, Y. W. 2007. Research Methods about Automatic Identification of Modern Chinese Abbreviation. Journal of Xinzhou Teachers University. 2 (Apr. 2007), 17--18&42.
[2]
Xu, S., Hou, F. W. and Bo, W. 2008. Predicting Chinese Abbreviations from Definitions: An Empirical Learning Approach Using Support Vector Regression. Journal of Computer Science and Technology. 4(Jul. 2008), 602--611.
[3]
Hou, F. W. 2011. Survey:Abbreviation Processing in Chinese Text. Journal of Chinese Information Processing. 5(Oct. 2011), 60--67&82. Tavel, P. 2007. Modeling and Simulation Design. AK Peters Ltd., Natick, MA.
[4]
Xu, S., Wen, J. L., Fan, Q. M. and Hou, F. W. 2013. Generalized Abbreviation Prediction with Negative Full Forms and Its Application on Improving Chinese Web Search. In Proceedings of International Joint Conference on Natural Language Processing. ACL, 641--647.
[5]
Jing, D. W. and Xiong, Z. Z. 2018. Rapid extraction algorithm of abbreviation based on reverse scanning and co-occurrence analysis. Application Research of Computers. 3 (Mar. 2018), 700--704.
[6]
Guo, Q. X. 1998. Semantic connection between abbreviations and original words. LANGUAGE PLANNING. 12 (Dec. 1998), 28--29.
[7]
Cui, Q. W. 2005. The relationship between abbreviations and their original words. Guangxi Social Sciences. 3 (Mar. 2005), 147--149.
[8]
Gai, M. J. L. 2016. A Contrastive Study of the Syntactic and the Semantic in Chinese Verbal Abbreviation and the Original Words. Postgraduate Thesis, Central China Normal University China(2016).
[9]
Tomas, M., Ilya, S., Kai, C., Greg, C. and Jeffrey, D. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems. ACM, 3111--3119.
[10]
Tomas, M., Kai, C. Greg, C. and Jeffrey, D. 2013. Efficient Estimation of Word Representations in Vector Space. Computer Science. 1--12.
[11]
Wen, C. Z. and Peng, X. 2013. Research on Chinese word Clustering with Word2vec.Software. 12 (Dec, 2013), 160--162.
[12]
Peng, W., Jia, M. X., Bo, X., Cheng, L. L. and Heng, Z. 2015. Semantic Clustering and Convolutional Neural Network for Short Text Categorization. In Proceeding of the 53rd Annual Meeting of the Association for Computational Linguistics. ACL. 351--356.
[13]
Yann, L. and Yoshua, B. 1995. Convolutional networks for images, speech, and time series. Handbook of Brain Theory & Neural Networks. MIT Press.
[14]
Jason, P. C. C. and Eric, N. 2015. Named Entity Recognition with Bidirectional LSTM-CNNs. Computer Science. 1--12.
[15]
Jun, Y. C., Caglar, G., Kyung, H. C. and Yoshua, B. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. Computer Science. 1--9.
[16]
Bao, Y. S., Yan, W. X., 1990, Chinese Abbreviations Dictionary, Foreign Language Teaching and Research Press.
[17]
Kun, Y., Yu, X., 1992, Practical Abbreviations Knowledge Dictionary, New World Press.

Cited By

View all
  • (2022)A Deep Learning Supported Sequential Recommendation Mechanism for Ban-Pick in MOBA Games2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)10.1109/SEAI55746.2022.9832346(259-265)Online publication date: 10-Jun-2022

Index Terms

  1. Semantic Relationship between Abbreviations and the Original Words Based on Word Vectors

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICIAI '19: Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence
    March 2019
    279 pages
    ISBN:9781450361286
    DOI:10.1145/3319921
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    In-Cooperation

    • Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University
    • University of Texas-Dallas: University of Texas-Dallas

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 15 March 2019

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. abbreviation
    2. abbreviation recognition
    3. semantic similarity
    4. word vector

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ICIAI 2019

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)4
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 17 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)A Deep Learning Supported Sequential Recommendation Mechanism for Ban-Pick in MOBA Games2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)10.1109/SEAI55746.2022.9832346(259-265)Online publication date: 10-Jun-2022

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media