计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 221-226.doi: 10.11896/jsjkx.210400135
李子仪, 周夏冰, 王中卿, 张民
LI Zi-yi, ZHOU Xia-bing, WANG Zhong-qing, ZHANG Min
摘要： 立场检测的主要目的是挖掘用户对话题或事件等的立场态度。与其他文本分类任务不同,立场的表达更隐晦,立场的态度对用户更加敏感。目前已有的立场检测方法主要是对话题内容自身信息进行建模,该类方法忽略了话题内容的用户背景信息,但用户及其喜好信息极大地影响着对文本信息的精准挖掘,通过关联用户信息能够获得潜在的信息特征。因此,提出了基于用户关联的立场检测模型(USDM),通过构建用户之间的图网络来建立用户关联结构,利用卷积操作挖掘相同用户下相似的文本立场信息,从全局的角度构建立场检测模型。同时,加入注意力机制以增强文本用户感知表示。在公开数据集H&N14上的实验结果表明,相比其他模型,所提模型获得了较好的性能。同时,消融实验表明,考虑用户关联以及注意力机制对检测准确率的提升具有重要作用。
中图分类号:
| [1]SOMASUNDARAN S,WIEBE J.Recognizing stances in ideo-logical on-line debates[C]//Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text.2010:116-124. [2]REN Y F,ZHANG Y,ZHANG M S,et al.Context-SensitiveTwitter Sentiment Classification Using Neural Network[C]//Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.2016:215-221. [3]DONG L,WEI F R,TAN C Q,et al.Adaptive recursive neural network for target-dependent twitter sentiment classification[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.2014:49-54. [4]SUN Q Y,WANG Z Q,ZHU Q M,et al.Stance Detection with Hierarchical Attention Network[C]//Proceedings of the 27th International Conference on Computational Linguistics.2018:2399-2409. [5]TANG D Y,QIN B,LIU T,et al.User modeling with neural network for review rating prediction[C]//Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence.2015:1340-1346. [6]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isAll You Need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems(NIPS’17).2017:6000-6010. [7]HASAN K S,NG V.Why are You Taking this Stance? Identi-fying and Classifying Reasons in Ideological Debates[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing.2014:751-762. [8]SRIDHAR D,FOULDS J R,HUANG B,et al.Joint models of disagreement and stance in online debate[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing.2015:116-125. [9]LI Y,CARAGEA C.Multi-Task Stance Detection with Sentiment and Stance Lexicons[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing.2019:6299-6305. [10]BURFOOT C,BIRD S,BALDWIN T.Collective classification of congressional floor-debate transcripts[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics.2011:1506-1515. [11]WALKER M A,ANAND P,ABBOTT R,et al.Stance classification using dialogic properties of persuasion[C]//Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2012:592-596. [12]RAJADESINGAN A,LIU H.Identifying users with opposingopinions in twitter debates[C]//Social Computing,Behavioral-Cultural Modeling and Prediction-7th International Conference.2014:153-160. [13]HASAN K S,NG V.Stance classification of ideological debates:Data,model,features,and constraints[C]//International Joint Conference on Natural Language Processing.2013:1348-1356. [14]AUGENSTEIN I,ROCKTASCHEL T,VLACHOS A,et al.Stance Detection with Bidirectional Conditional Encoding[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.2016:876-885. [15]ZHANG B,YANG M,LI X T,et al.Enhancing Cross-targetStance Detection with Transferable Semantic-Emotion Know-ledge[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.2020:3188-3197. [16]VIJAYARAGHAVAN P,VOSOUGHI S,ROY D.Automaticdetection and categorization of election-related tweets[C]//Proceedings of the International AAAI Conference on Web and Social Media.2016:703-706. [17]STEFANOV P,DARWISH K,ATANASOV A,et al.Predicting the Topical Stance and Political Leaning of Media using Tweets[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.2020:527-537. [18]HASAN K S,NG V.Extra-Linguistic Constraints on StanceRecognition in Ideological Debates[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics.2013:816-821. [19]CHEN W F,KU L W.UTCNN:a Deep Learning Model ofStance Classification on Social Media Text[C]//Proceedings of COLING 2016,the 26th International Conference on Computational Linguistics:Technical Papers.2016:11-17. [20]KOBBE J,HULPUS I,STUCKENSCHMIDT H.Unsupervised Stance Detection for Arguments from Consequences[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing.2020:50-60. [21]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[C]//International Conference on Learning Representations.2017:1-14. [22]MARCHEGGIANI D,TITOV I.Encoding Sentences withGraph Convolutional Networks for Semantic Role Labeling[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.2017:1506-1515. [23]YAO L,MAO C S,LUO Y.Graph Convolutional Networks for Text Classification[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019:7370-7377. [24]GHOSAL D,MAJUMDER N,PORIA S,et al.DialogueGCN:A Graph Convolutional Neural Network for Emotion Recognition in Conversation[J].arXiv:1908.11540v1,2019. [25]MARCHEGGIANI D,BASTINGS J,TITOV I.Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks[J].arXiv:1804.08313v2,2020. [26]WANG Z C,LV Q S,LAN X H,et al.Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.2018:349-357. [27]PENNINGTON J,SOCHER R,MANNING C D.GloVe:Global Vectors for Word Representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Proces-sing.2014:1532-1543. [28]LI C,PORCO A,GOLDWASSER D.Structured Representation Learning for Online Debate Stance Prediction[C]//Proceedings of the 27th International Conference on Computational Linguistics.2018:3728-3739. [29]CHANG C C,LIN C J.LIBSVM:A Library for Support Vector Machines[EB/OL].[2020-12-25].https://www.csie.ntu.edu.tw/~cjlin/libsvm/. [30]ALLAWAY E,MCKEOWN K R.Zero-Shot Stance Detection:A Dataset and Model using Generalized Topic Representations[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing.2020:8913-8931. |
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