Abstract
Traditional rule-based methods for recognizing Chinese temporal expressions present a lower recall rate and they cannot recognize the event-type Chinese temporal expressions, thus, we propose a new Chinese temporal expression recognition method through combining rules with a statistical model. Firstly, we divide Chinese temporal expressions into seven categories and use basic time units as the smallest unit of recognition to simplify the complexity of rule-making. Then, we use regular rules to recognize Chinese temporal expressions and label the training data automatically. Meanwhile, we label the event-type temporal expressions that rule-based method cannot recognize. Lastly, we use the labeled training data to learn a Conditional Random Fields model for Chinese temporal expression recognition. Experimental results show that our proposed method significantly reduces the amount of annotation work and effectively improves the recognition performance. The F1 value reaches 88.73%, which is higher than the rule-based method by 6.13%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zacks, J.M., Tversky, B.: Event structure in perception and conception. Psychol. Bull. 127(1), 3 (2001)
Pustejovsky, J., Lee, K., Bunt, H., Romary, L.: ISO-TimeML: an international standard for semantic annotation. In: LREC, vol. 10, pp. 394–397 (2010)
Liu, Z., Huang, M., Zhou, W.: Researcher on event-oriented ontology model. Comput. Sci. 36(11), 189–192 (2009)
Ge, T., Cui, L., Chang, B., Sui, Z., Zhou, M.: Event detection with burst information networks. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3276–3286 (2016)
Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M., Song, L.: Recurrent marked temporal point processes: embedding event history to vector. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1555–1564 (2016)
He, R., Qin, B., Liu, T., Pan, Y., Li, S.: Recognizing the extent of Chinese time expressions based on the dependency parsing and error-driven learning. J. Chin. Inf. Process. 21(5), 36–40 (2007)
Lin, J., Cai, D., Yuan, C.: Automatic timex2 tagging of Chinese temporal information. J. Tsinghua Univ. 48(1), 117–120 (2008)
Ferro, L., Gerber, L., Mani, I., Sundheim, B., Wilson, G.: TIDES 2005 standard for the annotation of temporal expressions (2005)
Wu, T., Zhou, Y., Huang, X., Wu, L.: Chinese time expression recognition based on automatically generated basic-time-unit rules. J. Chin. Inf. Process. 24(4), 3–11 (2010)
Zhu, S., Liu, Z., Fu, J., Zhu, F.: Chinese temporal phrase recognition based on conditional random fields. Comput. Eng. 37(15), 164–167 (2011)
Liu, L., He, Z., Xing, X., Mao, X.: Chinese time expression recognition based on semantic role. Appl. Res. Comput. 28(7), 2543–2545 (2011)
Wu, Q., Huang, D.: Temporal information extraction based on CRF and time thesaurus. J. Chin. Inf. Process. 28(6), 169–174 (2014)
Yan, Z., Ji, D.: Exploration of Chinese temporal information extraction based on CRF and semi-surprised learning. Comput. Eng. Des. 36(06), 1642–1646 (2015)
Verhagen, M., Sauri, R., Caselli, T., Pustejovsky, J.: SemEval-2010 task 13: TempEval-2. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 57–62 (2010)
Lafferty, J., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning, pp. 282–289. Morgan Kaufmann Publishers Inc. (2001)
Elliott, R.J., Aggoun, L., Moore, J.B.: Hidden Markov Models: Estimation and Control, vol. 29. Springer, New York (2008). https://doi.org/10.1007/978-0-387-84854-9
He, L., Lee, K., Lewis, M., Zettlemoyer, L.: Deep semantic role labeling: what works and what’s next. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 473–483 (2017)
Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles. Comput. Linguist. 28(3), 245–288 (2002)
Che, W., Li, Z., Liu, T.: LTP: a Chinese language technology platform. In: COLING 2010, 23rd International Conference on Computational Linguistics, Demonstrations, Beijing, China, vol. 23–27, pp. 13–16 (2010)
Acknowledgement
This work was supported by the National Key Research and Development Program of China [grant number 2018YFC1603601], the Program for Innovative Research Team in University of the Ministry of Education [grant number IRT17R32], and the National Natural Science Foundation of China [grant numbers [61673152, 91746209].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, M., Xia, J., Bao, X., Wu, G. (2019). Chinese Temporal Expression Recognition Combining Rules with a Statistical Model. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-26766-7_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26765-0
Online ISBN: 978-3-030-26766-7
eBook Packages: Computer ScienceComputer Science (R0)