Skip to main content

Advertisement

Springer Nature Link
Log in

Case2vec: joint variational autoencoder for case text embedding representation

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

The embedding representation of the case text represent text as vector which consist information of original texts abundantly. Text embedding representation usually uses text statistical features or content features alone. However, case texts have characteristics that include similar structure, repeated words, and different text lengths. And the statistical feature or content feature cannot represent case text efficiently. In this paper, we propose a joint variational autoencoder (VAE) to represent case text embedding representation. We consider the statistical features and content features of case texts together, and use VAE to align the two features into the same space. We compare our representations with existing methods in terms of quality, relationship, and efficiency. The experiment results show that our method has achieved good results, which have higher performance than the model using single feature.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Notes

  1. https://github.com/Maxpa1n/case2vec.

References

  1. Gururangan S, Dang T, Card D et al (2019) Variational pretraining for semi-supervised text classification. In: Proceedings of the 57th annual meeting of the association for computational linguistics. pp 5880–5894

  2. Zhao R, Mao K (2017) Fuzzy bag-of-words model for document representation. IEEE Trans Fuzzy Syst 26(2):794–804

    Article  Google Scholar 

  3. Ma S, Sun X, Wang Y et al (2018) Bag-of-words as target for neural machine translation. In: Proceedings of the 56th annual meeting of the association for computational linguistics, vol 2 (Short Papers). pp 332–338

  4. Trstenjak B, Mikac S, Donko D (2014) KNN with TF-IDF based framework for text categorization. Proc Eng 69:1356–1364

    Article  Google Scholar 

  5. Zhu Z, Liang J, Li D et al (2019) Hot topic detection based on a refined TF-IDF algorithm. IEEE Access 7:26996–27007

    Article  Google Scholar 

  6. Blei DM, Ng AY, Jordan MI et al (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  7. Johnson R, Zhang T (2015) Effective use of word order for text categorization with convolutional neural networks. In: Proceedings of the 2015 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL HLT 2015

  8. Naz S, Umar AI, Ahmad R et al (2017) Urdu Nasta’liq text recognition system based on multi-dimensional recurrent neural network and statistical features. Neural Comput Appl 28(2):219–231

    Article  Google Scholar 

  9. Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). pp 1746–1751

  10. Yang Z, Yang D, Dyer C et al (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies. pp 1480–1489

  11. Gupta P, Pagliardini M, Jaggi M (2019) Better word embeddings by disentangling contextual n-gram information. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, vol 1 (Long and Short Papers). pp 933–939

  12. Yang M et al (2018) Investigating capsule networks with dynamic routing for text classification. In: Proceedings of the 2018 conference on empirical methods in natural language processing

  13. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In NeurIPS

  14. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  15. Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. In NAACL-HLT

  16. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In NeurIPS

  17. Radford A, Narasimhan K, Salimans T, Sutskever I (2018) Improving language understanding by generative pre-training

  18. Devlin J, Chang M-W, Lee K, Toutanova K (2019). BERT: pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT

  19. Kingma DP, Welling M (2014) Auto-encoding variational bayes. Stat 1050:1

    MATH  Google Scholar 

  20. Bowman S, Vilnis L, Vinyals O, et al (2016) Generating sentences from a continuous space[C]. In: Proceedings of the 20th SIGNLL conference on computational natural language learning, p 10–21

  21. Yishu M, Yu L, Blunsom P (2016) Neural variational inference for text processing. In: International conference on machine learning

  22. Yang Z, Hu Z, Salakhutdinov R et al (2017) Improved variational autoencoders for text modeling using dilated convolutions. In: Proceedings of the 34th international conference on machine learning, vol 70. JMLR. org, pp 3881–3890

  23. Hoyle AM, Wolf-Sonkin L, Wallach H et al (2019) combining sentiment lexica with a multi-view variational autoencoder. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, vol 1 (Long and Short Papers). pp 635–640

  24. Zhao T, Zhao R, Eskenazi M (2017) Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. In: Proceedings of the 55th annual meeting of the association for computational linguistics, vol 1 (Long Papers). pp 654–664

  25. Kusner MJ, Paige B, Hernández-Lobato JM (2017) Grammar variational autoencoder. In: Proceedings of the 34th international conference on machine learning, vol 70. JMLR. org, pp 1945–1954

  26. Li X, Chen Z, Poon LKM et al (2019) Learning latent superstructures in variational autoencoders for deep multidimensional clustering. In: Proceedings of international conference on learning representations

  27. Paszke A, Gross S, Chintala S (2017) Automatic differentiation in PyTorch. In: Proceedings of the NIPS auto diff workshop. MIT Press

  28. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

  29. Yinhan L et al (2019) Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692

  30. Zhenzhong L et al (2020) ALBERT: a lite BERT for self-supervised learning of language representations. In: Proceedings of international conference on learning representations

Download references

Acknowledgements

The work was supported by National Key Research and Development Plan (Grant Nos. 2018YFC0830101, 2018YFC0830105, 2018YFC0830100), National Natural Science Foundation of China (Grant Nos. 61972186, 61761026, 61732005, 61672271 and 61762056), Yunnan high-tech industry development project (Grant No. 201606), Yunnan provincial major science and technology special plan projects: digitization research and application demonstration of Yunnan characteristic industry (Grant No. 202002AD080001-5), Yunnan Basic Research Project (Grant Nos. 202001AS070014, 2018FB104), and Talent Fund for Kunming University of Science and Technology (Grant No. KKSY201703005).

Author information

Authors and Affiliations

  1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China

    Ran Song, Shengxiang Gao, Zhengtao Yu, Yafei Zhang & Gaofeng Zhou

  2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China

    Ran Song, Shengxiang Gao, Zhengtao Yu, Yafei Zhang & Gaofeng Zhou

Authors
  1. Ran Song
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shengxiang Gao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Zhengtao Yu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yafei Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Gaofeng Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Shengxiang Gao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, R., Gao, S., Yu, Z. et al. Case2vec: joint variational autoencoder for case text embedding representation. Int. J. Mach. Learn. & Cyber. 12, 2517–2528 (2021). https://doi.org/10.1007/s13042-021-01335-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-021-01335-3

Keywords