计算机科学 ›› 2021, Vol. 48 ›› Issue (2): 245-249.doi: 10.11896/jsjkx.200100078
陈千1,2, 车苗苗1, 郭鑫1, 王素格1,2
CHEN Qian1,2, CHE Miao-miao1, GUO Xin1, WANG Su-ge1,2
摘要: 情感分类对推荐系统、自动问答、阅读理解等下游应用具有重要应用价值,是自然语言处理领域的重要研究方向。情感分类任务直接依赖于上下文,包括全局和局部信息,而现有的神经网络模型无法同时捕获上下文局部信息和全局信息。文中针对单标记和多标记情感分类任务,提出一种循环卷积注意力模型(LSTM-CNN-ATT,LCA)。该模型利用注意力机制融合卷积神经网络(Convolutional Neural Network,CNN)的局部信息提取能力和循环神经网络(Recurrent Neural Network,RNN)的全局信息提取能力,包括词嵌入层、上下文表示层、卷积层和注意力层。对于多标记情感分类任务,在注意力层上附加主题信息,进一步指导多标记情感倾向的精确提取。在两个单标记数据集上的F1指标达到82.1%,与前沿单标记模型相当;在两个多标记数据集上,小数据集实验结果接近基准模型,大数据集上的F1指标达到78.38%,超过前沿模型,表明LCA模型具有较高的稳定性和较强的通用性。
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[1] CHUNG J,GULCEHRE C,CHO K,et al.Empirical evaluation of gated recurrent neural networks on sequence modeling[J].arXiv:1412.3555,2014. [2] YIN W,KANN K,YU M,et al.Comparative study of cnn and rnn for natural language processing[J].arXiv:1702.01923,2017. [3] DOMINIK S,ANDREAS M,SVEN B.Evaluation of pooling ope-rations in convolutional architectures for object recognition[C]//International Conference on Artificial Neural Networks.2010:92-101. [4] RONAN C,JASON W,LEON B,et al.Natural language processing (almost) from scratch[J].Journal of Machine Learning Research,2011,12(1):2493-2537. [5] ZHU J,HASTIE T.Kernel logistic regression and the importvector machine[C]//International Conference on Neural Information Processing Systems:Natural & Synthetic.2001:1081-1088. [6] CORTES C,VAPNIK V.Support-vector networks[J].Machine Learning,1995,20(3):273-297. [7] LI Y,WEI B,LIU Y,et al.Incorporating knowledge into neural network for text representation[J].Expert Systems with Applications,2018,96(4):103-114. [8] KIM Y.Convolutional neural networks for sentence classification[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing.2014:1746-1751. [9] ALEX G.Generating Sequences with Recurrent Neural Net-works[J].arXiv:1308.0850,2013. [10] BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[J].arXiv:1409.0473,2016. [11] TANG D,QIN B,LIU T.Document modeling with gated recurrent neural network for sentiment classification[C]//Procee-dings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:1422-1432. [12] XU J,CHEN D,QIU X,et al.Cached long short-term memory neural networks for document-level sentiment classification[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.2016:1660-1669. [13] TIAN Z,RONG W,SHI L,et al.Attention Aware Bidirectional Gated Recurrent Unit Based Framework for Sentiment Analysis[C]//Proceedings of the 2018 Conference of the International Conference on Knowledge Science,Engineering and Management.Springer.Cham,2018:67-78. [14] DELVIN 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 Annual Confe-rence of the North American Chapter of the Association for Computational Linguistics.2019:4171-4186. [15] WANG Y,HUANG M,ZHAO L,et al.Attention-based lstm for aspect-level sentiment classification[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.2016:606-615. [16] CHENG J,ZHAO S,ZHANG J,et al.Aspect-level sentiment classification with HEAT (hierarchical attention) network[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.2017:97-106. [17] XUE W,LI T.Aspect based sentiment analysis with gated con-volutional networks[C]//Proceedings of the 56th Annual Mee-ting of the Association for Computational Linguistics.2018:2514-2523. [18] JIANG M,ZHANG W,ZHANG M,et al.An LSTM-CNN attention approach for aspect-level sentiment classification[J].Journal of Computational Methods in Sciences and Engineering,2019,(19):859-868. [19] QUOC L,TOMAS M.Distributed representations of sentences and documents[C]//International Conference on Machine Learning.2014:1188-1196. [20] JEFFREY P,RICHARD S,CHRISTOPHER M.Glove:Global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Proces-sing.2014:1532-1543. [21] KAI S,RICHARD S,CHRISTOPHER M.Improved semantic representations from tree-structured long short-term memory networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing,Association for Computational Linguistics (ACL).2015:1556-1566. [22] RICHARD S,ALEX P,JEAN W,et al.Recursive deep models for semantic compositionality over a sentiment treebank[C]//Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing.2013:1631-1642. [23] ZHANG R,HONGLAK L,DRAGOMIR R.Dependency sensitive convolutional neural networks for modeling sentences and documents[J].arXiv:1611.02361,2016. [24] ZHANG T,HUANG M,ZHAO L.Learning structured representation for text classification via reinforcement learning[C]//proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence.New Orleans,Louisiana,USA,2018. [25] YIN W,SCHUTZE H.Multichannel variable-size convolutionfor sentence classification[J].arXiv: 603.04513,2016. |
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