基于问题回答模式的中文事件抽取

doi:10.3778/j.issn.1002-8331.2107-0157

摘要/Abstract

摘要： 事件抽取是自然语言处理领域的一项基本任务。以问题回答模式进行事件抽取可以解决传统事件抽取方法存在的无法捕捉到不同事件类型中具有相似性的参数角色的语义信息等问题。目前相关学者以该模式提出的英文事件抽取方法受语言壁垒限制，其提出的问题模板在中文文本上提取效果不理想。为解决此问题，设计了一套符合中文事件抽取的问题模板的生成规则，选择BERT预训练模型作为中文事件抽取的基础模型，将问题回答模式应用到中文事件抽取任务中，并在ACE2005中文数据集进行测试。结果显示，在触发词识别、触发词分类、论元参数识别和论元参数的评价指标上，F1值分别达到77.7%、68.5%、51.5%和48.0%，在一定程度上验证了设计的问题模板的生成规则的有效性以及将问题回答模式应用到中文事件抽取任务中具有良好的抽取性能。

关键词: 事件抽取, 问题回答, 自然语言处理

Abstract: Event extraction is a basic task in the field of natural language processing. Event extraction in the question answering mode can solve the problem of traditional event extraction methods that cannot capture the semantic information of similar argument roles in different event types. At present, the English event extraction method proposed by related scholars in this mode is restricted by language barriers, and the question template proposed by them is not ideal for extracting Chinese texts. In order to solve this problem, a set of rules for generating question templates that conform to Chinese event extraction are designed. The BERT pre-training model is selected as the basic model for Chinese event extraction. The question answering model is applied to the Chinese event extraction task, and the ACE2005 Chinese Dataset for testing. The results show that the F1 value reaches 77.7%, 68.5%, 51.5%, and 48.0% in the evaluation indexes of Trigger Identification, Trigger Classification, Argument Identification, and Argument Classification. To a certain extent, it verifies the validity of the generated rules of the designed question template and the question answering mode of the Chinese event extraction task has good extraction performance.

Key words: event extraction, question answering, natural language processing

刘泽旖, 余文华, 洪智勇, 柯冠舟, 谭荣杰. 基于问题回答模式的中文事件抽取[J]. 计算机工程与应用, 2023, 59(2): 153-160.

LIU Zeyi, YU Wenhua, HONG Zhiyong, KE Guanzhou, TAN Rongjie. Chinese Event Extraction Using Question Answering[J]. Computer Engineering and Applications, 2023, 59(2): 153-160.

参考文献

[1] GRISHMAN R.Information extraction：techniques and challenges[C]//International Summer School on Information Extraction：A Multidisciplinary Approach to an Emerging Information Technology，1997：10-27.
[2] BANKS G C，WOZNYJ H M，WESSLEN R S，et al.A review of best practice recommendations for text analysis in R（and a user-friendly app）[J].Journal of Business and Psychology，2018，33（4）：445-459.
[3] AMIT S.Introducing the knowledge graph[R].America：Official Blog of Google，2012.
[4] SAMMON J W.A nonlinear mapping for data structure analysis[J].IEEE Transactions on Computers，1969，18（5）：401-409.
[5] CHEN P，SUN Z，BING L，et.al.Recurrent attention network on memory for aspect sentiment analysis[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing，2017：452-461.
[6] GRISHMAN R.Information extraction：capabilities and challenges[R].2012 International Winter School in Language and Speech Technologies，2012.
[7] DODDINGTON G R，MITCHELL A，PRZYBOCKI M A，et al.The automatic content extraction（ACE） program-tasks，data，and evaluation[C]//Proceedings of the Fourth International Conference on Language Resources and Evaluation，2004：837-840.
[8] CHEN Y，XU L，LIU K，et al.Event extraction via dynamic multi-pooling convolutional neural network[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.
[9] NGUYEN T H，CHO K，GRISHMAN R.Joint event extraction via recurrent neural networks[C]//Proceedings of NAACL-HLT，2016：300-309.
[10] CHEN C，NG V.Joint modeling for Chinese event extraction with rich linguistic features[C]//The COLING 2012 Organizing Committee，2012：529-544.
[11] ZENG Y，YANG H，FENG Y，et al.A convolution BiLSTM neural network model for chinese event extraction[C]//National CCF Conference on Natural Language Processing and Chinese Computing，2016：275-287.
[12] HOCHREITER S，SCHMIDHUBER J.Long short-term memory[J].Neural Computation，1997，9（8）：1735-1780.
[13] LIN H，LU Y，HAN X，et.al.Joint Chinese event extraction based multitask learning[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics，2018：1565-1574.
[14] DEVLIN J，CHANG M W，LEE K，et al.BERT：pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies，2019：4171-4186.
[15] XU N，XIE H，ZHAO D.A novel joint framework for multiple Chinese events extraction[C]//Proceedings of the 19th China National Conference on Computional Linguistics，2020：950-961.
[16] HUANG W，ZHANG J，JI D.A transition-based neural framework for Chinese information extraction[J].Plos One，2020：1-15.
[17] CHEN Y，CHEN T，EBNER S，et al.Reading the manual：event extraction as definition comprehension[C]//Proceedings of 4th Workshop on Structured Prediction for NLP，2020：74-83.
[18] DU X，CARDIE C.Event extraction by answering（almost） natural questions[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing（EMNLP），2020：671-683.
[19] CHEN D，FISCH A，WESTON J，et al.Reading wikipedia to answer open-domain questions[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics，2017：1870-1879.
[20] VASWANI A，SHAZEER N，PARMAR N，et al.Attention is all you need[C]//Proceedings of the 31st Conference on Neural Information Processing Systems，2017：6000-6010.
[21] ZHANG J，QIN Y，ZHANG Y，et al.Extracting entities and events as a single task using a transition-based neural model[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence，2019：5422-5428.
[22] SRIVASTAVA N，HINTON G，KRIZHEVSKY A，et al.Dropout：a simple way to prevent neural networks from overfitting[J].Journal of Machine Learning Research，2014，15：1929-1958.
[23] DING N，LI Z，LIU Z，et al.Event detection with trigger-aware lattice neural network[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing，2019：347-356.

[24] LIU Y，OTT M，GOYAL N，et al.RoBERTa：a robustly optimized BERT pretraining appoch[C]//Proceedings of the ICLR 2020 Conference Program Chairs，2020：1-15.

[25] SENNRICH R，HADDOW B，BIRCH A.Neural machine translation of rare words with subword units[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics，2016：1715-1725.