Skip to main content
SpringerLink
Log in
Book cover

Workshop on Chinese Lexical Semantics

CLSW 2020: Chinese Lexical Semantics pp 398–407Cite as

Multi-task Learning Neural Networks for Comparative Elements Extraction

  • Conference paper
  • First Online:

  • 1465 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12278))

Abstract

Comparative sentences are common in human languages. In online comments, a comparative sentence usually contains the subjective attitude or emotional tendency of a reviewer. Hence, comparative elements extraction (CEE) is valuable for opinion mining and sentiment analysis. Most of the existing CEE systems use rule-based or machine learning approaches that need to construct a rule base or spend a huge amount of effort on feature engineering. These approaches usually involve multiple steps, and the performance of each step relies on the accuracy of the previous step, risking error cascading oversteps. In this paper, we adopt a neural network approach to CEE, which supports end-to-end training and automatic learning of sentence representation. Furthermore, considering the high relevance of CEE and comparative sentences recognition (CSR), we propose a multi-task learning model to combine the two tasks, which can further improve the performance of CEE. Experiment results show that both our neural network approach and multi-task learning are effective for CEE.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    https://github.com/google-research/bert.

References

  1. Ding, C.: A Course for Mandarin Chinese Grammar, pp. 81–95. Peking University Press, Beijing (2009)

    Google Scholar 

  2. Wang, S., Zhao, G., Liu, H.: Comparison element ellipsis identification based on rules and sequence patterns. J. Shanxi Univ. (Nat. Sci. Ed.) 38, 85–92 (2015)

    Google Scholar 

  3. Jindal, N., Bing, L.: Mining comparative sentences and relations. In: Proceedings of the 21st National Conference on Artificial Intelligence, pp. 1331–1336. AAAI Press, Menlo Park (2006)

    Google Scholar 

  4. Song, R., Lin, H., Chang, F.: Chinese comparative sentences identification and comparative relations extraction. J. Chin. Inf. Process. 23, 102–107 (2009)

    Google Scholar 

  5. Wang, W., Zhao, T., Xin, G., Xu, Y.: Extraction of comparative elements using conditional random fields. Acta Automatica Sinica 41, 1385–1393 (2015)

    Google Scholar 

  6. Jacob, D., Chang, M., Kenton, L., Kristina, T.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 4171–4186. ACL, Stroudsburg (2019)

    Google Scholar 

  7. Zhou, H., Hou, M., Hou, M., Teng, Y.: Chinese comparative sentences identification and comparative elements extraction based on semantic classification. J. Chin. Inf. Process. 28, 136–141 (2014)

    Google Scholar 

  8. Kessler, W., Kuhn, J.: Detection of product comparisons-how far does an out-of-the-box semantic role labeling system take you? In: Proceedings of the 2013 Conference on Empirical Methods on Natural Language Processing, pp. 1892–1897. ACL, Stroudsburg (2013)

    Google Scholar 

  9. Hou, F., Li, G.: Mining chinese comparative sentences by semantic role labeling. In: Proceedings of the 2008 International Conference on Machine Learning and Cybernetics, pp. 2563–2568. IEEE, Piscataway (2008)

    Google Scholar 

  10. Xing, L., Liu, L.: Chinese standard comparative sentence recognition and extraction research. In: Proceedings of the 2013 International Conference on Information Engineering and Applications, pp. 415−422. Springe, London (2013)

    Google Scholar 

  11. Huang, G., Yao, T., Liu, Q.: Mining Chinese comparative sentences and relations based on CRF algorithm. Appl. Res. Comput. 27, 2061–2064 (2010)

    Google Scholar 

  12. John, L., Andrew, M., Fernando, P.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference Machine Learning, pp. 282−289. ACM, New York (2001)

    Google Scholar 

  13. Bai, L., Hu, R., Liu, Z.: Recognition of comparative sentences based on syntactic and semantic rules-system. Acta Scientiarum Naturalium Universitatis Pekinensis 51, 275–281 (2015)

    Google Scholar 

  14. Zhang, C., Feng, C., Liu, Q., Shi, C., Huang, H., Zhou, H.: Chinese comparative sentence identification based on multi-feature fusion. J. Chin. Inf. Process. 27, 110–116 (2013)

    Google Scholar 

  15. Yoon, K.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. ACL, Stroudsburg (2014)

    Google Scholar 

  16. Liadh, K., et al.: Overview of the CLEF eHealth evaluation lab 2019. In: Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2019. Lecture Notes in Computer Science, vol. 11696, pp. 322−339. Springer, Cham (2019)

    Google Scholar 

  17. Yu, J., Jiang, J.: Adapting BERT for target-oriented multimodal sentiment classification. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pp. 5408−5414. Morgan Kaufmann, San Francisco (2019)

    Google Scholar 

  18. Rich, A.C.: Multitask learning: a knowledge-based source of inductive bias. In: Proceedings of the Tenth International Conference Machine Learning, pp. 41−48. ACM, New York (1993)

    Google Scholar 

  19. Li, Q., Ji, H.: Incremental joint extraction of entity mentions and relations. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 402–412. ACL, Stroudsburg (2014)

    Google Scholar 

  20. Yuan, Z., David, W.: Stack-propagation: improved representation learning for syntax. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 1557–1566. ACL, Stroudsburg (2016)

    Google Scholar 

  21. Tomas, M., Ilya, S., Kai, C., Gregory, S.C., Jeffrey, D.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, NIPS 2013, vol. 26, pp. 3111–3119. MIT Press, Cambridge (2013)

    Google Scholar 

  22. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Parallel distributed processing: explorations in the microstructure of cognition, vol. 1, pp. 318–362. ACM, New York (1986)

    Google Scholar 

Download references

Acknowledgements

This research project is supported by the National Natural Science Foundation of China (61872402), the Humanities and Social Science Project of the Ministry of Education (17YJAZH068), Science Foundation of Beijing Language and Culture University (supported by ‘‘the Fundamental Research Funds for the Central Universities’’) (18ZDJ03), the Open Project Program of the National Laboratory of Pattern Recognition (NLPR), the Fundamental Research Funds for the Central Universities, and Research Funds of Beijing Language and Culture University (20YCX147).

Author information

Authors and Affiliations

  1. School of Information Science, Beijing Language and Culture University, Beijing, 10083, China

    Dianqing Liu, Lihui Wang & Yanqiu Shao

Authors
  1. Dianqing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lihui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanqiu Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanqiu Shao .

Editor information

Editors and Affiliations

  1. Department of Linguistics and Translation, City University of Hong Kong, Hong Kong SAR, China

    Meichun Liu

  2. Department of Linguistics and Translation, City University of Hong Kongg, Hong Kong SAR, China

    Chunyu Kit

  3. School of Foreign Languages, Peking University, Beijing, China

    Qi Su

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, D., Wang, L., Shao, Y. (2021). Multi-task Learning Neural Networks for Comparative Elements Extraction. In: Liu, M., Kit, C., Su, Q. (eds) Chinese Lexical Semantics. CLSW 2020. Lecture Notes in Computer Science(), vol 12278. Springer, Cham. https://doi.org/10.1007/978-3-030-81197-6_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-81197-6_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-81196-9

  • Online ISBN: 978-3-030-81197-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation