Skip to main content
SpringerLink
Log in

Acr2Vec: Learning Acronym Representations in Twitter

  • Conference paper
  • First Online:
Rough Sets (IJCRS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10313))

Included in the following conference series:

  • 1132 Accesses

Abstract

Acronyms are common in Twitter and bring in new challenges to social media analysis. Distributed representations have achieved successful applications in natural language processing. An acronym is different from a single word and is generally defined by several words. To this end, we present Acr2Vec, an algorithmic framework for learning continuous representations for acronyms in Twitter. First, a Twitter ACRonym (TACR) dataset is automatically constructed, in which an acronym is expressed by one or more definitions. Then, three acronym embedding models have been proposed: MPDE (Max Pooling Definition Embedding), APDE (Average Pooling Definition Embedding), and PLAE (Paragraph-Like Acronym Embedding). The qualitative experimental results (i.e., similarity measure) and quantitative experimental results (i.e., acronym polarity classification) both show that MPDE and APDE are superior to PLAE.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Notes

  1. 1.

    http://www.netlingo.com/acronyms.php.

  2. 2.

    http://www.noslang.com/dictionary/.

  3. 3.

    http://www.acronymfinder.com/.

  4. 4.

    http://www.urbandictionary.com/.

  5. 5.

    http://nlp.stanford.edu/projects/glove/.

References

  1. Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent Twitter sentiment classification. In: Proceedings of ACL: HLT, pp. 151–160 (2011)

    Google Scholar 

  2. Ren, F., Wu, Y.: Predicting user-topic opinions in Twitter with social and topical context. IEEE Trans. Affect. Comput. 4(4), 412–424 (2013)

    Article  Google Scholar 

  3. Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. J. Artif. Intell. Res. 50, 723–762 (2014)

    Article  Google Scholar 

  4. Wu, F., Song, Y., Huang, Y.: Microblog sentiment classification with contextual knowledge regularization. In: Proceedings of AAAI, pp. 2332–2338 (2015)

    Google Scholar 

  5. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of ICLR (2013)

    Google Scholar 

  6. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of EMNLP, pp. 1532–1543 (2014)

    Google Scholar 

  7. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of ICML, pp. 1188–1196 (2014)

    Google Scholar 

  8. Li, C., Ji, L., Yan, J.: Acronym disambiguation using word embedding. In: Proceedings of AAAI, pp. 4178–4179 (2015)

    Google Scholar 

  9. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of EMNLP, pp. 79–86 (2002)

    Google Scholar 

  10. Wang, S., Manning, C.D.: Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of ACL, pp. 90–94 (2012)

    Google Scholar 

  11. Turney, P.D., Littman, M.L.: Measuring praise and criticism: inference of semantic orientation from association. ACM Trans. Inf. Syst. 21(4), 315–346 (2003)

    Article  Google Scholar 

  12. Gruhl, D., Nagarajan, M., Pieper, J., Robson, C., Sheth, A.: Multimodal social intelligence in a real-time dashboard system. VLDB J. 19(6), 825–848 (2010)

    Article  Google Scholar 

  13. Mohammad, S., Kiritchenko, S., Zhu, X.: NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. In: Proceedings of SemEval, pp. 321–327 (2013)

    Google Scholar 

  14. Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    MATH  Google Scholar 

Download references

Acknowledgments

This work is partially supported by the National Natural Science Foundation of China (No. 61673301, No. 61573255) and the Open Research Funds of State Key Laboratory for Novel Software Technology (No. KFKT2017B22).

Author information

Authors and Affiliations

  1. Research Center of Big Data and Network Security, Tongji University, Shanghai, 200092, People’s Republic of China

    Zhifei Zhang & Shuwen Ma

  2. Center of Educational Technology and Computing, Tongji University, Shanghai, 200092, People’s Republic of China

    Zhifei Zhang & Shuwen Ma

  3. Department of Computer Science and Technology, Tongji University, Shanghai, 201804, People’s Republic of China

    Zhifei Zhang & Sheng Luo

  4. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, People’s Republic of China

    Zhifei Zhang

Authors
  1. Zhifei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sheng Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuwen Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sheng Luo .

Editor information

Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, SK, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Zhang, Z., Luo, S., Ma, S. (2017). Acr2Vec: Learning Acronym Representations in Twitter. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60837-2_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60836-5

  • Online ISBN: 978-3-319-60837-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation