Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2020, 3(3); doi: 10.25236/AJCIS.2020.030309.

Entity recognition of Chinese bidding announcement title based on deep learning

Author(s)

Kong Zhang

Corresponding Author:
Kong Zhang
Affiliation(s)

College of Information Engineering,Nanjing University of Finance and Economics,  210023, China
418921061@qq.com

Abstract

Bidding announcement widely exists in Chinese government procurement websites at all levels in the form of text. Its composition is complex and the number is numerous. Identifying and extracting more concise project names is helpful to improve the ability of website data query and analysis. To solve this problem, this paper proposes a Transformer-att-label model based on deep learning. The model uses Transformer-att for feature extraction. It uses the attention mechanism to replace the original multi-head combination of Transformer, which can improve the recognition effect. And combined with tag embedding, predict the tag semantics of words, and select the tag closest to its semantics for output. The proposed model was tested on the announcement title data set of the Chinese bidding website, and the recognition effect of other mainstream models was compared to verify the effectiveness of the method.

Keywords

Chinese project name, named entity recognition, deep learning, Transformer, label

Cite This Paper

Kong Zhang. Entity recognition of Chinese bidding announcement title based on deep learning. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 3: 83-91. https://doi.org/10.25236/AJCIS.2020.030309.

References

[1] Chinchor N (1995). MUC-6 named entity task definition (version 2.1) [C]. Proceedings of the 6th Conference on Message Under-standing, Columbia, Maryland.
[2] Collins M, Singer Y (1999). Unsupervised models for named entity classification [C]. Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora: 100-110.
[3] Zhou G D, Su J (2002). Named entity recognition using an HMM-based chunk tagger[C]. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics.  Association for Computational Linguistics: 473-480.
[4] Chieu H L, Ng H T (2002). Named entity recognition: a maximum entropy approach using global information [C]. In: Proceedings of the 19th international conference on Computational linguistics. Association for Computational Linguistics: 1-7.
[5] Borthwick A, Grishman R (1999). A maximum entropy approach to named entity recognition [D].  New York University, Graduate School of Arts and Science,:1-11.
[6] Isozaki H, Kazawa H (2002). Efficient support vector classifiers for named entity recognition [C]. Proceedings of the 19th Interna-tional Conference on Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 1: 1-7.
[7] Ekbal A, BandyopadhyayS (2010). Named entity recognition using support vector machine: A language independent approach [J]. International Journal of Electrical, Computer, and Systems Engineering, 4 (2): 155-170.
[8] Lafferty J, McCallum A, Pereira F C N (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data [J].:1-15.
[9] McCallum A, Li W (2003). Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons [C]. In: Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003. Association for Computational Linguistics, 2003: 188-191.
[10] Le Cun Y, Bengio Y, Hinton G (2015). Deep learning [J]. Nature, 521 (7553): 436-444.
[11] Hirschberg J, Manning C D (2015). Advances in natural language processing [J]. Science, 349 (6245): 261-266.
[12] Pinheiro P H O, ColloBert R (2013). Recurrent Convolutional Neural Networks for Scene Parsing [J].
[13] Peng N, Dredze M (2016). Improving named entity recognition for Chinese social media with word segmentation representation learning [C]. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics 149-155.
[14] Zhou G D, Su J (2002). Named entity recognition using an HMM-based chunk tagger[C]. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics.  Association for Computational Linguistics,: 473-480.
[15] Chieu H L, Ng H T (2002). Named entity recognition: a maximum entropy approach using global information [C]. In: Proceedings of the 19th international conference on Computational linguistics. Association for Computational Linguistics,: 1-7.