research-article

Chinese relation extraction based on lattice network improved with BERT model

Authors:

Zhengchun Zhang,

Qingsong YuAuthors Info & Claims

ICMAI '20: Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence

Pages 98 - 102

https://doi.org/10.1145/3395260.3395276

Published: 29 May 2020 Publication History

Abstract

Relation classification is a basic and important task in the field of natural language processing(NLP). There are already many researches on English dataset, but the researches on Chinese dataset are very few. Due to the particularity of Chinese language, most existing methods suffer from the two main problems of segmentation error and polysemy. To sum up, the problem of segmentation error can be solved fairly well by many models, take lattice model for example, which can segment Chinese word precisely. But the problem of polysemy has not received enough attention. In this paper, we take advantage of BERT model to deal with the problem of polysemy. The experimental results show that our model achieves good result and outperforms baseline model.

References

[1]

Zhang, Y., & Yang, J. (2018). Chinese NER using lattice LSTM. arXiv preprint arXiv:1805.02023.

[2]

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735--1780.

[3]

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

[4]

Mintz, M., Bills, S., Snow, R., & Jurafsky, D. (2009, August). Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2 (pp. 1003--1011). Association for Computational Linguistics.

[5]

Ji, G., Liu, K., He, S., & Zhao, J. (2017, February). Distant supervision for relation extraction with sentence-level attention and entity descriptions. In Thirty-First AAAI Conference on Artificial Intelligence.

Digital Library

[6]

Feng, X., Guo, J., Qin, B., Liu, T., & Liu, Y. (2017, August). Effective Deep Memory Networks for Distant Supervised Relation Extraction. In IJCAI (pp. 4002--4008).

[7]

Liu, C., Sun, W., Chao, W., & Che, W. (2013, December). Convolution neural network for relation extraction. In International Conference on Advanced Data Mining and Applications (pp. 231--242). Springer, Berlin, Heidelberg.

Digital Library

[8]

Zeng et al., 2014. Zeng, D., Liu, K., Lai, S., Zhou, G., and Zhao, J. (2014). Relation classification via convolutional deep neural network. In Proceedings of COLING, pages 2335--2344.

[9]

Santos, C. N. D., Xiang, B., & Zhou, B. (2015). Classifying relations by ranking with convolutional neural networks. arXiv preprint arXiv:1504.06580.

[10]

Zhang, D., & Wang, D. (2015). Relation classification via recurrent neural network. arXiv preprint arXiv:1508.01006.

[11]

Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., & Xu, B. (2016, August). Attention-based bidirectional long short-term memory networks for relation classification. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 207--212).

[12]

Wang, L., Cao, Z., De Melo, G., & Liu, Z. (2016, August). Relation classification via multi-level attention cnns. In Proceedings of the 54th annual meeting of the Association for Computational Linguistics (volume 1: long papers) (pp. 1298--1307).

[13]

Li, Z., Ding, N., Liu, Z., Zheng, H., & Shen, Y. (2019, July). Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 4377--4386).

[14]

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

[15]

Xu, J., Wen, J., Sun, X., & Su, Q. (2017). A discourse-level named entity recognition and relation extraction dataset for Chinese literature text. arXiv preprint arXiv:1711.07010.

[16]

Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

[17]

Zeng, D., Liu, K., Chen, Y., & Zhao, J. (2015, September). Distant supervision for relation extraction via piecewise convolutional neural networks. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.

[18]

J Lin, Y., Shen, S., Liu, Z., Luan, H., & Sun, M. (2016, August). Neural relation extraction with selective attention over instances. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 2124--2133).

Cited By

Zhengyu HXiuqin Z(2023)A Precise Text-to-Diagram Generation Method for Elementary Geometry2023 20th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)10.1109/ICCWAMTIP60502.2023.10387090(1-7)Online publication date: 15-Dec-2023
https://doi.org/10.1109/ICCWAMTIP60502.2023.10387090
Lai QDing SGong JCui JLiu S(2022)A Chinese Multi-modal Relation Extraction Model for Internet Security of Finance2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)10.1109/DSN-W54100.2022.00029(123-128)Online publication date: Jun-2022
https://doi.org/10.1109/DSN-W54100.2022.00029

Index Terms

Chinese relation extraction based on lattice network improved with BERT model
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

Recommendations

FinEst BERT and CroSloEngual BERT: Less Is More in Multilingual Models
Text, Speech, and Dialogue
Abstract
Large pretrained masked language models have become state-of-the-art solutions for many NLP problems. The research has been mostly focused on English language, though. While massively multilingual models exist, studies have shown that monolingual ...
Chinese Relation Extraction with Bi-directional Context-Based Lattice LSTM
Knowledge Science, Engineering and Management
Abstract
Chinese entity relation extraction (Chinese RE) is a crucial task for various NLP applications. It aims to automatically extract relationships between entities in Chinese texts, thereby enhancing the accuracy of natural language understanding. ...
A semantic lexicon-based approach for sense disambiguation and its WWW application
ICIC'09: Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications

This work proposes a basic framework for resolving sense disambiguation through the use of Semantic Lexicon, a machine readable dictionary managing both word senses and lexico-semantic relations. More specifically, polysemous ambiguity characterizing ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICMAI '20: Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence

April 2020

252 pages

ISBN:9781450377072

DOI:10.1145/3395260

Copyright © 2020 ACM.

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

Southwest Jiaotong University
Xihua University: Xihua University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 May 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICMAI 2020

ICMAI 2020: 2020 5th International Conference on Mathematics and Artificial Intelligence

April 10 - 13, 2020

Chengdu, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
216
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhengyu HXiuqin Z(2023)A Precise Text-to-Diagram Generation Method for Elementary Geometry2023 20th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)10.1109/ICCWAMTIP60502.2023.10387090(1-7)Online publication date: 15-Dec-2023
https://doi.org/10.1109/ICCWAMTIP60502.2023.10387090
Lai QDing SGong JCui JLiu S(2022)A Chinese Multi-modal Relation Extraction Model for Internet Security of Finance2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)10.1109/DSN-W54100.2022.00029(123-128)Online publication date: Jun-2022
https://doi.org/10.1109/DSN-W54100.2022.00029

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten