Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 93-97.
• Intelligent Computing • Previous Articles Next Articles
ZHANG Lu, SHEN Chen-lin, LI Shou-shan
CLC Number:
[1]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. [2]COLLOBERT R,WESTON J,BOTTOU L,et al.Natural language processing(almost) from scratch [J].Journal of Machine Learning Research,2011,12(1):2493-2537. [3]TURIAN J,RATINOV L,BENGIO Y.Word representations:a simple and general method for semi-supervised learning[C]∥Proceedings of the Meeting of the Association for Computational Linguistics.2010:384-394. [4]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space [J].arXiv:1301.3781. [5]TANG J,QU M,WANG M,et al.LINE:large-scale information network embedding[C]∥Proceedings of the International World Wide Web Conference.2015:1067-1077. [6]黄磊,李寿山,周国栋.基于句法信息的微博情绪识别方法研究 [J].计算机科学,2017,44(2):244-249. [7]LIU H H,LI S S,ZHOU G D,et al.Joint modeling of news reader’s and comment writer’s emotions[C]∥Proceedings of the Meeting of the Association for Computational Linguistics.2013:511-515. [8]ABDUL-MAGEED M,UNGAR L.EmoNet:fine-grained emotion detection with gated recurrent neural networks[C]∥Proceedings of the Meeting of the Association for Computational Linguistics.2017:718-728. [9]LI S S,HUANG L,WANG R,et al.Sentence-level emotion classification with label and context dependence[C]∥Procee-dings of the Meeting of the Association for Computational Linguistics.2015:1045-1053. [10]KOZAREVA Z,NAVARRO B,VAZQUEZ S,et al.UA-ZBSA:a headline emotion classification through web information[C]∥Proceedings of theInternational Workshop on Semantic Evaluations.Association for Computational Linguistics.2007:334-337. [11]WEN S,WAN X.Emotion classification in microblog texts using class sequential rules[C]∥Proceedings of theAAAI Conference on Artificial Intelligence.2014:187-193. [12]LI S S,HUANG L,WANG R,et al.Sentence-level emotion classification with label and context dependence[C]∥Procee-dings of the Meeting of the Association for Computational Linguistics.2015:1045-1053. [13]ALM C C,ROTH D,SPROAT R.Emotions from text:machine learning for text-based emotion prediction[C]∥Proceedings of the Conference on Empirical Methods in Natural Language Processing.2005:579-586. [14]LI C X,WU H M,JIN Q.Emotion classification of Chinese microblog text via fusion of bow and evector feature representations [C]∥Communications in Computer and Information Scie-nce.2014:217-228. [15]LI S S,XU J,ZHANG D,et al.Two-view label propagation to semi-supervised reader emotion classification[C]∥Proceedings of theInternational Conference on Computational Linguistics.2016:2647-2655. [16]BENGIO Y,DUCHARME R,VINCENT P,et al.A neural probabilistic language model[J].Journal of Machine Learning Research,2003,3:1137-1155. [17]MNIH A,HINTON G.A scalable hierarchical distributed language model [C]∥Proceedings of theInternational Conference on Neural Information Processing Systems.2008:1081-1088. [18]SOCHER R,BAUER J,MANNING C D,et al.Parsing with compositional vector grammars[C]∥Proceedings of the Mee-ting of the Association for Computational Linguistics.2013:455-165. [19]TANG D Y,QIN B,LIU T,et al.Learning sentence representa-tion for emotion classification on microblogs[C]∥Proceedings of the Meeting of theNatural Language Processing and Chinese Computing.2013:212-223. [20]XU R F,CHEN T,XIA Y Q,et al.Word embedding composition for data imbalances in sentiment and emotion classification [J].Cognitive Computation,2015,7(2):226-240. [21]WANG Z Q,ZHANG Y,LEE S Y M,et al.A bilingual attention network for code-switched emotion prediction[C]∥Proceedings of theInternational Conference on Computational Linguistics.2016:1624-1634. [22]LABUTOV I,LIPSON H.Re-embedding words[C]∥Procee-dings of the Meeting of the Association for Computational Linguistics.2013:489-493. [23]HUANG L,LI S S,ZHOU G D.Emotion corpus construction on microblog[C]∥Proceedings of the Chinese Lexical Semantics Workshop.2015:204-212. [24]NIU F,RECHT B,RE C,et al.Hogwild:a lock-free approach to parallelizing stochastic gradient descent[C]∥Proceedings of theInternational Conference on Neural Information Processing Systems.2011:693-701. [25]TANG J,QU M,MEI Q Z.PTE:predictive text embedding through large-scale heterogeneous text networks[C]∥Procee-dings of the Knowledge Discovery in Database.2015:1165-1174. [26]HOCHREITER S,SCHMIDHUBER J.Long short-term memory [J].Neural Computation,1997,9(8):1735-1780. [27]GRAVES A.Generating sequences with recurrent neural net-works [J].arXiv:1308.0850. |
[1] | HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163. |
[2] | HAN Hong-qi, RAN Ya-xin, ZHANG Yun-liang, GUI Jie, GAO Xiong, YI Meng-lin. Study on Cross-media Information Retrieval Based on Common Subspace Classification Learning [J]. Computer Science, 2022, 49(5): 33-42. |
[3] | LI Hao, ZHANG Lan, YANG Bing, YANG Hai-xiao, KOU Yong-qi, WANG Fei, KANG Yan. Fine-grained Sentiment Classification of Chinese Microblogs Combining Dual Weight Mechanismand Graph Convolutional Neural Network [J]. Computer Science, 2022, 49(3): 246-254. |
[4] | DING Feng, SUN Xiao. Negative-emotion Opinion Target Extraction Based on Attention and BiLSTM-CRF [J]. Computer Science, 2022, 49(2): 223-230. |
[5] | LI Yu-qiang, ZHANG Wei-jiang, HUANG Yu, LI Lin, LIU Ai-hua. Improved Topic Sentiment Model with Word Embedding Based on Gaussian Distribution [J]. Computer Science, 2022, 49(2): 256-264. |
[6] | YUAN Jing-ling, DING Yuan-yuan, SHENG De-ming, LI Lin. Image-Text Sentiment Analysis Model Based on Visual Aspect Attention [J]. Computer Science, 2022, 49(1): 219-224. |
[7] | HU Yan-li, TONG Tan-qian, ZHANG Xiao-yu, PENG Juan. Self-attention-based BGRU and CNN for Sentiment Analysis [J]. Computer Science, 2022, 49(1): 252-258. |
[8] | LIU Kai, ZHANG Hong-jun, CHEN Fei-qiong. Name Entity Recognition for Military Based on Domain Adaptive Embedding [J]. Computer Science, 2022, 49(1): 292-297. |
[9] | LI Zhao-qi, LI Ta. Query-by-Example with Acoustic Word Embeddings Using wav2vec Pretraining [J]. Computer Science, 2022, 49(1): 59-64. |
[10] | DAI Hong-liang, ZHONG Guo-jin, YOU Zhi-ming , DAI Hong-ming. Public Opinion Sentiment Big Data Analysis Ensemble Method Based on Spark [J]. Computer Science, 2021, 48(9): 118-124. |
[11] | ZHANG Jin, DUAN Li-guo, LI Ai-ping, HAO Xiao-yan. Fine-grained Sentiment Analysis Based on Combination of Attention and Gated Mechanism [J]. Computer Science, 2021, 48(8): 226-233. |
[12] | SHI Wei, FU Yue. Microblog Short Text Mining Considering Context:A Method of Sentiment Analysis [J]. Computer Science, 2021, 48(6A): 158-164. |
[13] | PAN Fang, ZHANG Hui-bing, DONG Jun-chao, SHOU Zhao-yu. Aspect Sentiment Analysis of Chinese Online Course Review Based on Efficient Transformer [J]. Computer Science, 2021, 48(6A): 264-269. |
[14] | YU Sheng, LI Bin, SUN Xiao-bing, BO Li-li, ZHOU Cheng. Approach for Knowledge-driven Similar Bug Report Recommendation [J]. Computer Science, 2021, 48(5): 91-98. |
[15] | LI Jian-lan, PAN Yue, LI Xiao-cong, LIU Zi-wei, WANG Tian-yu. Chinese Commentary Text Research Status and Trend Analysis Based on CiteSpace [J]. Computer Science, 2021, 48(11A): 17-21. |
|