Abstract
Event is the basic unit of knowledge representation, when human beings communicate with each other. Although event is objective in the real world, when it is described by voice, characters and other means, event has some language features. We treated event as the basic unit of knowledge representation for Chinese news texts. We pre-processed event data in the CEC2.0 corpus from positional feature, Chinese POS tagging feature and other aspects. And then, we proposed minding core word association rules for event language expression from CEC2.0 corpus with Apriori algorithm and optimized them. we detected event elements from Chinese news texts with the mined association rules, including action, object and time. The value of F1 is 60.7%.
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Acknowledgments
This work was supported by the Project from National Natural Science Foundation of China under Grant 61672006, in part by the Talent Project from Fuyang Normal University under Grant 2018kyqd0027, in part by the Key Project of University Science Research in Anhui under Grant KJ2018A0328. And we would like to thank the reviewers for their beneficial comments and suggestions, which improves the paper.
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Wang, X., Wang, S., Sun, G., Chen, X., Wang, X., Liu, Z. (2020). Apriori Algorithm-Based Association Rules of Event Language Expression Discovery. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019. ATCI 2019. Advances in Intelligent Systems and Computing, vol 1017. Springer, Cham. https://doi.org/10.1007/978-3-030-25128-4_154
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