计算机科学 ›› 2019, Vol. 46 ›› Issue (7): 151-156.doi: 10.11896/j.issn.1002-137X.2019.07.024
沈忱林,张璐,吴良庆李寿山
SHEN Chen-lin,ZHANG Lu,WU Liang-qing,LI Shou-shan
摘要： 情感分类是自然语言处理研究中的一项基本任务,旨在判别文本的情感极性。目前,情感分类相关研究主要针对句子、篇章和微博等文本形式。与以往研究不同的是,文中面向新颖的问答型评论展开情感分类。首先,收集并标注了大规模、高质量的问答型评论语料集;针对问答型评论的特点,提出了一种基于双向注意力机制的神经网络方法。具体而言,该方法首先通过双向LSTM对问题文本和答案文本分别编码,再通过双向注意力机制同时计算问题文本和答案文本的情感权重,最后通过情感权重计算得到问答型评论的情感匹配信息。实验结果表明,提出的方法在问答情感分类任务上达到了75.5%的准确率和61.4%的F1值,相较于其他基准方法有明显的提升。
中图分类号:
| [1]ZHAO Y Y,QIN B,LIU T.Sentiment Analysis [J].Journal of Software,2010,21(8):1834-1848.(in Chinese) 赵妍妍,秦兵,刘挺.文本情感分析 [J].软件学报,2010,21(8):1834-1848. [2]ZHANG Y F,LAI G K,ZHANG M,et al.Explicit Factor Mo- dels for Explainable Recommendation Based on phrase-level Sentiment Analysis[C]∥Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval.2014:83-92. [3]CHAMBERS N,BOWEN V,GENCO E,et al.Identifying Politi- cal Sentiment between Nation States with Social Media[C]∥Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:65-75. [4]WANG R H,CUI X M,ZHOU W,et al.Research of Text Sen- timent Classification Based on Improved Semantic Comprehension [J].Computer Science,2017,44(S2):92-97.(in Chinese) 王日宏,崔兴梅,周炜,等.改进的基于语义理解的文本情感分类方法研究 [J].计算机科学,2017,44(S2):92-97. [5]XU J C,CHEN D L,QIU X P,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. [6]TURKEY P D.Thumbs up or Thumbs down?:Semantic Orientation Applied to Unsupervised Classification of Reviews[C]∥Proceedings of the 40th Annual Meeting on Association for Computational Linguistics.2002:417-424. [7]TANG D Y,WEI F R,YANG N,et al.Learning Sentiment Specific Word Embedding for Twitter Sentiment Classification[C]∥Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.2014:1555-1565. [8]MATSUMOTO S,TAKAMURA H,OKUMURA M.Senti- ment Classification Using Word Sub-sequence and Dependency Sub-trees[C]∥Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining.2005:301-311. [9]ZHOU X J,WAN X J,XIAO J G.Cross-lingual Sentiment Classification with Bilingual Document Representation Learning[C]∥Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.2016:1403-1412. [10]LI S S,HUANG C R,ZHOU G D,et al.Employing Personal/Impersonal Views in Supervised and Semi-supervised Sentiment Classification[C]∥Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics.2010:414-423. [11]XIA R,WANG C,DAI X Y,et al.Co-training for Semi-supervised Sentiment Classification Based on Dual-view Bags-of-words Representation[C]∥Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing.2015:1054-1063. [12]PONTIKI M,GALANIS D,PAVLOPOULOS J,et al.SemEval-2014 Task 4:Aspect Based Sentiment Analysis[C]∥Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016).2016:19-30. [13]WANG Y Q,HUANG M L,ZHAO L,et al.Attention-based LSTM for Aspect-level Sentiment Classification[C]∥Procee-dings of the 2016 Conference on Empirical Methods in Natural Language Processing.2016:606-615. [14]TANG D Y,QIN B,LIU T.Aspect Level Sentiment Classification with Deep Memory Network[C]∥Proceedings of the 2016 Conference on Empirical Methods in Natural Language Proces-sing.2016:214-224. [15]HOCHREITER S,SCHMIDHUBER J.Long Short-term Me- mory [J].Neural Computation,1997,9(8):1735-1780. [16]LUONG T,PHAM H,MANNINGC D.Effective Approaches to Attention-based Neural Machine Translation[C]∥Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:1412-1421. [17]PENG Y,HE X,ZHAO J.Object-part Attention Model for Fine-grained Image Classification [J].IEEE Transactions on Image Processing,2018,27(3):1487-1500. [18]YANG Z C,YANG D Y,DYER C,et al.Hierarchical Attention Networks for Document Classification[C]∥Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2016:1480-1489. [19]CUI Y M,CHEN Z P,WEI S,et al.Attention-over-attention Neural Networks for Reading Comprehension[C]∥Proceedings of the 55th Annual Meeting of the Association for Computatio-nal Linguistics.2017:593-602. [20]KINGMA D,BA J.Adam:A Method for Stochastic Optimization [C]∥Proceedings of the 3rd International Conference on Learning Representations.2015:1-15. [21]LEI T,BARZILAY R,JAAKKOLA T.Molding CNNs for Text:Non-linear,Non-consecutive Convolutions[C]∥Procee-dings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:1565-1575. [22]WANG Z G,HAMZA W,FLORIAN R.Bilateral Multi-perspec- tive Matching for Natural Language Sentences[C]∥Proceedings of the 2017 International Joint Conferences on Artificial Intelligence.2017:4144-4150. |
| [1] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
| [2] | 周芳泉, 成卫青. 基于全局增强图神经网络的序列推荐 Sequence Recommendation Based on Global Enhanced Graph Neural Network 计算机科学, 2022, 49(9): 55-63. https://doi.org/10.11896/jsjkx.210700085 |
| [3] | 戴禹, 许林峰. 基于文本行匹配的跨图文本阅读方法 Cross-image Text Reading Method Based on Text Line Matching 计算机科学, 2022, 49(9): 139-145. https://doi.org/10.11896/jsjkx.220600032 |
| [4] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
| [5] | 熊丽琴, 曹雷, 赖俊, 陈希亮. 基于值分解的多智能体深度强化学习综述 Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization 计算机科学, 2022, 49(9): 172-182. https://doi.org/10.11896/jsjkx.210800112 |
| [6] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
| [7] | 汪鸣, 彭舰, 黄飞虎. 基于多时间尺度时空图网络的交通流量预测模型 Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction 计算机科学, 2022, 49(8): 40-48. https://doi.org/10.11896/jsjkx.220100188 |
| [8] | 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153 |
| [9] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
| [10] | 闫佳丹, 贾彩燕. 基于双图神经网络信息融合的文本分类方法 Text Classification Method Based on Information Fusion of Dual-graph Neural Network 计算机科学, 2022, 49(8): 230-236. https://doi.org/10.11896/jsjkx.210600042 |
| [11] | 金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190 |
| [12] | 熊罗庚, 郑尚, 邹海涛, 于化龙, 高尚. 融合双向门控循环单元和注意力机制的软件自承认技术债识别方法 Software Self-admitted Technical Debt Identification with Bidirectional Gate Recurrent Unit and Attention Mechanism 计算机科学, 2022, 49(7): 212-219. https://doi.org/10.11896/jsjkx.210500075 |
| [13] | 彭双, 伍江江, 陈浩, 杜春, 李军. 基于注意力神经网络的对地观测卫星星上自主任务规划方法 Satellite Onboard Observation Task Planning Based on Attention Neural Network 计算机科学, 2022, 49(7): 242-247. https://doi.org/10.11896/jsjkx.210500093 |
| [14] | 张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036 |
| [15] | 曾志贤, 曹建军, 翁年凤, 蒋国权, 徐滨. 基于注意力机制的细粒度语义关联视频-文本跨模态实体分辨 Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism 计算机科学, 2022, 49(7): 106-112. https://doi.org/10.11896/jsjkx.210500224 |
|
||
首页