Abstract
In the field of government affairs, Appointment and Dismissal Relation Extraction (ADRE) of officials from personnel news is crucial for updating government knowledge. However, ADRE faces great challenges, including extremely high accuracy demand, lack of data, tuple sparsity and conflict, and requiring incremental update. To address these challenges, we propose an Incremental Bootstrapping approach for Relation Extraction (IBRE) and apply it to real-time updating of personnel knowledge in government-affair Knowledge Graphs. IBRE starts with few seeds and trains with pattern generation, pattern evaluation, tuple prediction and seed augmentation in an iterative and incremental manner. First, we design a new strategy for defining seeds as document-tuple pairs to eliminate the effects of tuple sparsity and conflict. Then, a new definition of patterns with both word and part-of-speech is proposed to get high accuracy. Finally, we augment seeds with corrected tuples and apply incremental learning to continually improve performance with least training cost. We build a dataset called ADNP (Appointment and Dismissal News from People.cn) and compare our approach with baselines. Comparison results show that our approach performs the best. Moreover, experimental results demonstrate that incremental learning continuously improves the performance of IBRE.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
renshi.people.com.cn.
- 2.
References
Agichtein, E., Gravano, L.: Snowball: extracting relations from large plain-text collections. Proc. ACM DL 2000, 85–94 (2000)
Brin, S.: Extracting patterns and relations from the world wide web. In: Atzeni, P., Mendelzon, A., Mecca, G. (eds.) WebDB 1998. LNCS, vol. 1590, pp. 172–183. Springer, Heidelberg (1999). https://doi.org/10.1007/10704656_11
Hoffmann, R., Zhang, C., Ling, X., Zettlemoyer, L., Weld, D.S.: Knowledge-based weak supervision for information extraction of overlapping relations. Proc. ACL-HLT 2011, 541–550 (2011)
Jian, X., Zhixiong, Z., Zhenxin, W.: Review on techniques of entity relation extraction. Data Anal. Knowl. Disc. 24(8), 18–23 (2008)
Jiao, Z., Sun, S., Sun, K.: Chinese lexical analysis with deep Bi-GRU-CRF network. arXiv pp. arXiv-1807 (2018)
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. Proc. ACL-IJCNLP 2009, 1003–1011 (2009)
Mooney, R.J., Bunescu, R.C.: Subsequence kernels for relation extraction. NIPS 2006, 171–178 (2006)
Nguyen, T.H., Grishman, R.: Relation extraction: perspective from convolutional neural networks. Proc. NAACL-HLT 2015, 39–48 (2015)
Pawar, S., Palshikar, G.K., Bhattacharyya, P.: Relation extraction: a survey. arXiv preprint arXiv:1712.05191 (2017)
dos Santos, C., Xiang, B., Zhou, B.: Classifying relations by ranking with convolutional neural networks. In: Proceedings of ACL-IJCNLP 2015, vol. 1, Long Papers, pp. 626–634 (2015)
Socher, R., Huval, B., Manning, C.D., Ng, A.Y.: Semantic compositionality through recursive matrix-vector spaces. Proc. EMNLP-CoNLL 2012, 1201–1211 (2012)
Song, Y., Shi, S., Li, J., Zhang, H.: Directional skip-gram: explicitly distinguishing left and right context for word embeddings. In: Proceedings of NAACL HLT 2018, vol. 2 (Short Papers), pp. 175–180 (2018)
Yao, L., Haghighi, A., Riedel, S., McCallum, A.: Structured relation discovery using generative models. In: proceedings of EMNLP 2011, pp. 1456–1466 (2011)
Yoo, S., Jeong, O.: Automating the expansion of a knowledge graph. Expert Syst. Appl. 141, 112965 (2020)
Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. J. Mach. Learn. Res. 3, 1083–1106 (2003)
Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: Proceedings of COLING 2014: Technical Papers, pp. 2335–2344 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mao, L., Wang, M., Li, C., Fan, J., Hou, C. (2021). IBRE: An Incremental Bootstrapping Approach for Chinese Appointment and Dismissal Relation Extraction. In: Chen, H., Liu, K., Sun, Y., Wang, S., Hou, L. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence. CCKS 2020. Communications in Computer and Information Science, vol 1356. Springer, Singapore. https://doi.org/10.1007/978-981-16-1964-9_11
Download citation
DOI: https://doi.org/10.1007/978-981-16-1964-9_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1963-2
Online ISBN: 978-981-16-1964-9
eBook Packages: Computer ScienceComputer Science (R0)