Skip to main content
SpringerLink
Log in

Thai Named-Entity Recognition Using Variational Long Short-Term Memory with Conditional Random Field

  • Conference paper
  • First Online:
Advances in Intelligent Informatics, Smart Technology and Natural Language Processing (iSAI-NLP 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 807))

Abstract

Thai Named-Entity Recognition (NER) is a difficult task due to the characteristics of Thai language such as the lack of special character that separates named-entity from other word types. Previous Thai NER system heavily depends on human’s knowledge in a form of feature selection and external resources such as dictionaries. A recent trend in NER research shows that deep learning approach can be used to train high-quality NER system without resorting on these external resources. In this paper, we present a deep learning model that combines recurrent neural networks with a probabilistic graphical model, as well as, a variational inference-based dropout approach. We benchmark our model on one of the largest Thai corpora called “BEST 2010”. Our model outperforms all baseline methods without relying on extra manually annotated resources and external knowledge.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Note that each entity is tagged with BIO format. Therefore, each named-entity can be separated into two classes. For example, B-PER and I-PER are two different classes.

  2. 2.

    These NE tags are not mentioned in [16], and BEST2010 documentation does not provide further explanation.

References

  1. Chanlekha, H., Kawtrakul, A.: Thai named entity extraction by incorporating maximum entropy model with simple heuristic information. In: Proceedings of the IJCNLP (2004)

    Google Scholar 

  2. Suwanno, N., Suzuki, Y., Yamazaki, H.: Selecting the optimal feature sets for Thai named entity extraction. In: Proceedings of ICEE-2007 & PEC, vol. 5 (2007)

    Google Scholar 

  3. Tirasaroj, N., Aroonmanakun, W.: Thai named entity recognition based on conditional random fields. In: Eighth International Symposium on Natural Language Processing, SNLP 2009, pp. 216–220. IEEE (2009)

    Google Scholar 

  4. Tirasaroj, N., Aroonmanakun, W.: The effect of answer patterns for supervised named entity recognition in Thai. In: PACLIC 2011, pp. 392–399 (2011)

    Google Scholar 

  5. Collobert, J., Weston, L., Bottou, M., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)

    Google Scholar 

  6. Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)

  7. Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Trans. Assoc. Comput. Linguist. 4, 357–370 (2016)

    Google Scholar 

  8. Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016)

  9. Lafferty, J., et al.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML, vol. 1, pp. 282–289 (2001)

    Google Scholar 

  10. Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT press, Cambridge (2009)

    Google Scholar 

  11. Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  14. Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 1019–1027 (2016)

    Google Scholar 

  15. Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  16. Aw, A., Mahani, S.A., Lertcheva, N., Kalunsima, S.: TaLAPi - a Thai linguistically annotated corpus for language processing. In: LREC, pp. 125–132 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Department of Computer Engineering, Chulalongkorn University, Bangkok, Thailand

    Can Udomcharoenchaikit & Peerapon Vateekul

  2. NECTEC, Language and Semantic Technology Lab (LST), Pathumthani, Thailand

    Prachya Boonkwan

Authors
  1. Can Udomcharoenchaikit
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peerapon Vateekul
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prachya Boonkwan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Can Udomcharoenchaikit .

Editor information

Editors and Affiliations

  1. Sirindhorn International Institute of Technology (SIIT), Thammasat University, Bangkadi Muang Pathumthani, Thailand

    Thanaruk Theeramunkong

  2. Faculty of Science and Technology, Department of Computer Science, Thammasat University, Pathumthani, Thailand

    Rachada Kongkachandra

  3. Department of Information Technology Management, Faculty of Information Technology , King Mongkut’s University of Technology North Bangkok (KMUTNB), Bangkok, Thailand

    Mahasak Ketcham

  4. Department of Computer Engineering, Faculty of Engineering, Mahidol University, Nakorn Pathom, Thailand

    Narit Hnoohom

  5. Faculty of Science and Technology, Department of Computer Science, Thammasat University , Pathum Thani, Thailand

    Pokpong Songmuang

  6. National Science and Technology Development Agency, Language and Semantic Technology Laboratory, National Electronics and Computer Technology Center, Pathum Thani, Thailand

    Thepchai Supnithi

  7. Prince of Songkla University , Phuket, Thailand

    Kiyota Hashimoto

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Udomcharoenchaikit, C., Vateekul, P., Boonkwan, P. (2019). Thai Named-Entity Recognition Using Variational Long Short-Term Memory with Conditional Random Field. In: Theeramunkong, T., et al. Advances in Intelligent Informatics, Smart Technology and Natural Language Processing. iSAI-NLP 2017. Advances in Intelligent Systems and Computing, vol 807. Springer, Cham. https://doi.org/10.1007/978-3-319-94703-7_8

Download citation

Publish with us

Policies and ethics

Search

Navigation