Abstract:A deep convolutional neural networks (DCNNs) combined with long-short term memory (LSTM) is proposed to extract the emergency events in Uyghur text. The method extracts six major feature blocks that are included in emergency events and employs word embedding. Using the DCNNs to extract the high level local features of the event sentence as the input,this method captures the sequence relations in the event sentence via LSTM,and train a Softmax classifier to accomplish the task. The accuracy of the method is 80.60%,the recall 81.39%,and the F value 80.99%.
[1] Haddow G,Bullock J,Coppola D P.Introduction to Emergency management (Fifth Edition)[M].Wiley Subscription Services,Inc.A Wiley Company,2013. [2] Bahdanau D,Cho K,Bengio Y.Neural machine translation by jointly learning to Align and Translate[J].Computer Science,2014:1-15. [3] Tang D,Qin B,Liu T.Document modeling with gated recurrent neural network for sentiment classification[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing,2015:1422-1432. [4] Mina M,Bansal M.End-to-end relation extraction using LSTMs on sequences and tree structures[C]//Proceedings of the 54th Annual Meeting of the Associaton for Computational Linguisties.2016:1105-1116. [5] 孙晓,高飞,任福继,等.基于深度模型的社会新闻对用户情感影响挖掘[J].中文信息学报,2017,31(3):184-190. [6] 孙晓,何家劲,任福继.基于多特征融合的混合神经网络模型讽刺语用判别[J].中文信息学报,2016,30(6):215-223. [7] 李冬白,田生伟,禹龙,等.基于深度学习的维吾尔语人称代词指代消解[J].中文信息学报,2017,31(4):80-88. [8] Lu W,Roth D.Automatic event extraction with structured preference modeling[C]//Proceedings of Meeting of the Association for Computational Linguistics:Long Papers.2013:835-844. [9] Li Q,Ji H,Huang L.Joint Event extraction via structured prediction with global features[C]//Proceedings of Meeting of the Association for Computational Linguistics,2013:73-82. [10] Liao S,Grishman R.Using document level cross-event Inference to improve event extraction[C]//Proceedings of Meeting of the Association for Computational Linguistics,2010:789-797. [11] Ji H,Grishman R.Refining event extraction through cross-document inference[C]//Proceedings of Meeting of the Association for Computational Linguistics,2008:254-262. [12] Thien Huu Nguyen,Ralph Grishman.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(Short Papers),2015:365-371. [13] 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. [14] Liu Y,Wei F,Li S,et al.A Dependency Based neural network for relation classification[J].Computer Science,2015:285-290. [15] Neubauer C.Shape,position and size invariant visual pattern recognition based on principles of neocognitron and perceptron[OL].1992.https://www.mysciencework.com. [16] Graves A.Generating sequences with recurrent neural networks[J].Computer Science,2013:1-43. [17] Mikolov T,Sutskever I,Chen K,et al.Distributed representations of words and phrases and their compositionality[J].Advances in Neural Information Processing Systems,2013(26):3111-3119.