HRTNSC: Hybrid Representation-based Subjective and Objective Sentence Classification for Tibetan News
KONG Chunwei1,2 , LYU Xueqiang1,2, ZHANG Le2
1.School of Computer Science, Qinghai Normal University, Xi'ning, Qinghai 810008, China; 2.Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China
Abstract：Focused on Tibetan news texts, this paper proposes a hybrid representation-based subjective and objective sentence classification model (HRTNSC). The input layer is enriched by fusing syllable-level features and word-level features. The BiLSTM+CNN network is applied to the subjective and objective classification of sentences. The experimental results show that the HRTNSC model achieves an optimal F1 value of 90.84%, which is better than the benchmark model.
[1] Wiebe J,Wilson T,Bruce R,et al. Learning subjective language[J]. Computational Linguistics,2004,30(3): 277-308. [2] 闫晓东,黄涛. 基于情感词典的藏语文本句子情感分类[J]. 中文信息学报,2018,32(2): 75-80. [3] Tsai Y,Bai S,Liang P P,et al. Multimodal transformer for unaligned multimodal language sequences[C]//Proceedings of 57th Annual Meeting of the Association for Computational Linguistics,2019: 6558-6569. [4] 张晓梅,李茹,王斌,等. 基于融合特征的微博主客观分类方法[J]. 中文信息学报,2014,28(4): 50-57. [5] He R D,Lee W S,Ng H T,et al. Effective attention modeling for aspect-level sentiment classification[C]//Proceedings of the 27th International Conference on Computational Linguistics,2018: 1121-1131. [6] 李卫疆,漆芳,余正涛. 基于多通道特征和自注意力的情感分类方法[J]. 软件学报,2021,32(9): 2783-2800. [7] Peng M,Ma R,Zhang Q,et al. Simplify the usage of lexicon in Chinese NER[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics,2020: 5951-5960. [8] 何炎祥,孙松涛,牛菲菲,等. 用于微博情感分析的一种情感语义增强的学习模型[J]. 计算机学报,2017,40(4): 773-790. [9] 刘培玉,荀静,费绍栋,等. 基于隐马尔科夫模型的主观句识别[J]. 中文信息学报,2016,30(4): 206-212. [10] Huddar M G,Sannakki S S,Rajpurohit V S. Multi-evel feature optimization and multimodal contextual fusion for sentiment analysis and emotion classification[J]. Computational Intelligence,2020,36(5): 135-146. [11] Huang B,Carley K M. Syntax-aware aspect level sentiment classification with graph attention networks[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing,2019: 5472-5480. [12] Majumder N,Poria S,Gelbukh A F,et al. IARM: inter aspect relation modeling with memory networks in aspect-based sentiment analysis[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing,2018: 3402-3411. [13] 张乐. 微博突发事件检测与情感分析研究[D]. 北京: 北京信息科技大学硕士学位论文,2014. [14] Song Y,Wang J,Jiang T,et al. Attentional encoder network for targeted sentiment classification[J/OL]. arXiv preprint arXiv: 1902.09314,2019. [15] 王剑,唐珊,黄于欣,等. 基于句子关联图的汉越双语多文档新闻观点句识别[J]. 计算机应用,2020,40(10): 2845-2849. [16] Bahdanau D ,Cho K,Bengio Y. Neural machine translation by jointly learning to align and translate[J/OL]. arXiv preprint arXiv: 1409.0473,2014. [17] 黄彪. 案件相关的新闻观点句抽取及情感分析方法研究[D]. 昆明: 昆明理工大学硕士学位论文,2020. [18] 林思琦,余正涛,郭军军,等. 融入多特征的汉越新闻观点句抽取方法[J]. 中文信息学报,2019,33(11): 101-106. [19] Mikolov T,Chen K,Corrado G,et al. Efficient estimation of word representations in vector space[J/OL]. arXiv preprint arXiv: 1301.3781,2013. [20] Pennington J,Socher R,Manning C D. GloVe: Global vectors for word representation[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing,2014: 1532-1543. [21] 柔特,才让加. 基于循环卷积神经网络的藏文句类识别[J]. 中文信息学报,2019,33(12): 76-82. [22] Ban M,Cai Z,Cai R,et al. Titetan interrogative sentence recognition and classification based on phrase features[C]//Proceedings of the 2nd International Conference on Computer Science,Communication and Network Security,2020: 502-512. [23] 孙萌,华却才让,才智杰,等. 基于判别式分类和重排序技术的藏文分词[J]. 中文信息学报,2014,28(2): 61-65. [24] 色差甲,慈祯嘉措,才让加,等. 基于神经网络的藏文正字检错法[J]. 中文信息学报,2020,34(12): 48-53. [25] Devlin J,Chang M W,Lee K,et al. BERT: Pretraining of deep bidirectional transformers for language understanding[C]//Proceeding of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,2019: 4171-4183.
2022, Vol. 36 
