Abstract：Event detection is one of the important tasks in the field of information extraction. In this paper, Chinese event detection is regarded as a sequence labeling rather than a classification problem. A Chinese event detection model ATT-BiLSTM is proposed, which integrates attention mechanism and long short-term memory neural network. The attention mechanism is used to better capture global features and BiLSTM layers are employed to capture sequence features more effectively. Experiments on the ACE 2005 Chinese dataset show that the performance of the proposed method significantly outperforms other existing Chinese event detection methods.
[1]Zeng Y, Yang H, Feng Y, et al. A convolution BiLSTM neural network model for Chinese event extraction[C]//Proceedings of the 5th CCF Conference on Natural Language Understanding and Intelligent Applications, 2016: 275-287. [2]Feng X, Huang L, Tang D, et al. A language-independent neural network for event detection[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016: 66-71. [3]Chen Z, Ji H. Language specific issue and feature exploration in Chinese event extraction[C]//Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2009: 209-212. [4]Yangarber R. Scenario customization for information extraction[D]. New York University, 2001. [5]姜吉发. 一种跨语句汉语事件信息抽取方法[J]. 计算机工程, 2005,31(2):27-29. [6]谭红叶. 中文事件抽取关键技术研究[D]. 哈尔滨: 哈尔滨工业大学博士学位论文, 2008. [7]Liu J, Chen Y, Liu K, et al. Event detection via gated multilingual attention mechanism[C]//Proceedings of the AAAI, 2018: 4865-4872. [8]David Ahn. The stages of event extraction[C]//Proceedings of the Workshop on Annotations and Reasoning about Time and Events. USA: Association for Computational Linguistics, 2006:1-8. [9]Grishman R, Westbrook D, Meyers A. NYU’s English ACE 2005 system description[C]//Proceedings of ACE 2005 Evaluation Workshop. Washington, US, 2005:05-19. [10]Hardy H, Kanchakouskaya V, Strzalkowski T. Automatic event classification using surface text features[C]//Proceedings of AAAI Workshop on Event Extraction and Synthesis. Boston, USA: American Association for Artificial Intelligence, 2006:31-27. [11]赵妍妍,秦兵,车万翔,等. 中文事件抽取技术研究[J]. 中文信息学报, 2008,22(1):3-8. [12]Naughton M, Kushmerick N, Carthy J. Event extraction from heterogeneous news sources[C]//Proceedings of AAAI Workshop on Event Extraction and Synthesis. Boston, US: American Association for Artificial Intelligence, 2006:7-13. [13]Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio. Neural machine translation by jointly learning to align and translate[C]//Proceedings of the International Conference on Learning Representations(ICLR). San Diego, CA. 2014, arXiv preprint arXiv:1409.0473. [14]Zeng D, Liu K, Lai S, et al. Relation classification via convolutional deep neural network[C]//Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 2014: 2335-2344. [15]Tang D, Qin B, Liu T. Document modeling with gated recurrent neural network for sentiment classification[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015:1422-1432. [16]Chen Y, Xu L, Liu K, et al. Event extraction via dynamic multi-pooling convolutional neural networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 2015: 167-176. [17]Nguyen T H, Grishman R. Event detection and domain adaptation with convolutional neural networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 2015: 365-371. [18]Nguyen T H, Grishman R. Modeling skip-grams for event detection with convolutional neural networks[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016: 886-891. [19]Nguyen T H, Fu L, Cho K, et al. A two-stage approach for extending event detection to new types via neural networks[C]//Proceedings of the 1st Workshop on Representation Learning for NLP, 2016: 158-165. [20]Nguyen T H, Cho K, Grishman R. Joint event extraction via recurrent neural networks[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016: 300-309. [21]Zhang Z, Xu W, Chen Q. Joint Event extraction based on skip-window convolutional neural networks[C]//Proceedings of the 5th CCF Conference on Natural Language Understanding and Intelligent Applications. Springer, Cham, 2016: 324-334. [22]Sha L, Qian F, Chang B, et al. Jointly extracting event triggers and arguments by dependency-bridge RNN and Tensor-based argument interaction[C]//Proceedings of the 32nd AAAI Conference on Artifiual Intelligences 2018: 5916-5923. [23]Lin H, Lu Y, Han X, et al. Nugget proposal networks for Chinese event detection[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018: 1565-1574. [24]Linguistic Data Consortium. ACE (Automatic Content Extraction) Chinese annotation guidelines for events[EB/OL]. [2018-12-13]. http://www.ldc.upenn.edu/Projects/ACE. [25]Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[C]//Proceedings of International Conference on Learning Representation, 2013:1-12. [26]Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. [27]金宸,李维华,姬晨,等. 基于双向LSTM神经网络模型的中文分词[J]. 中文信息学报, 2018, 32(2):29-37. [28]Schuster M, Paliwal K K. Bidirectional recurrent neural networks[J].IEEE Transactions on Signal Processing, 1997, 45(11): 2673-2681. [29]Treisman A M, Gelade G. A feature-integration theory of attention[J]. Cognitive Psychology, 1980, 12(1): 97-136. [30]Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Proceedings of Advances in Neural Information Processing Systems, 2017: 5998-6008. [31]Chen C, Ng V. Joint modeling for Chinese event extraction with rich linguistic features[C]//Proceedings of COLING 2012, 2012: 529-544.
2019, Vol. 33 
