[1]WEI Siyu,ZHU Guangli,TAN Guangpu,et al.Ironic sentence recognition model integrating ironic language features[J].CAAI Transactions on Intelligent Systems,2024,19(3):689-696.[doi:10.11992/tis.202209021]
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Ironic sentence recognition model integrating ironic language features
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
19
Number of periods:
2024 3
Page number:
689-696
Column:
学术论文—自然语言处理与理解
Public date:
2024-05-05
- Title:
- Ironic sentence recognition model integrating ironic language features
- Keywords:
- ironic sentence recognition; language features; Chi-square test algorithm; Word2Vec; bidirectional gated recursive neural unit; attention mechanism; attention mechanism; deep learning; intelligent information processing
- CLC:
- TP391
- DOI:
- 10.11992/tis.202209021
- Abstract:
- Irony is a method of expressing sentiment implicitly. Differences between the words and the emotions of ironic sentences are abundant, causing difficulty in the sentiment classification of ironic sentences. To solve this problem, an ironic sentence recognition model integrating ironic language features (ISR) is proposed to improve the recognition accuracy of the ironic sentence by adding ironic language features. Initially, the Chi-square test algorithm is used to analyze ironic language and obtain language features. Then, Word2Vec is used to train the language features to obtain the feature representation of the language features. At the same time, the attention mechanism and Bi-GRU (bidirectional gated recursive neural unit) model are used to obtain the feature representation of the sentence. Finally, the feature representations of language features and sentences are fused as the input of the sentiment classification layer to identify the ironic sentences. The model has been compared with CNN-AT, CNN-Adv, and EPSN models. Experiment results show that the proposed model has high recognition accuracy for the ironic sentence.
- References:
-
[1] ZHANG Chuxu, SONG Dongjin, HUANG Chao, et al. Heterogeneous graph neural network[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Anchorage: ACM, 2019: 793-803.
[2] YAO Liang, MAO Chengsheng, LUO Yuan. Graph convolutional networks for text classification[J]. Proceedings of the AAAI conference on artificial intelligence, 2019, 33(1): 7370–7377.
[3] CHEN Junjie, HOU Hongxu, JI Yatu, et al. Graph convolutional networks with structural attention model for aspect based sentiment analysis[C]//2019 International Joint Conference on Neural Networks. Budapest: IEEE, 2019: 1-7.
[4] WANG Baoxin. Disconnected recurrent neural networks for text categorization[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg: Association for Computational Linguistics, 2018: 2311-2320.
[5] SUN Zhiqing, YU Hongkun, SONG Xiaodan, et al. MobileBERT: a compact task-agnostic BERT for resource-limited devices[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Online. Stroudsburg: Association for Computational Linguistics, 2020: 2158-2170.
[6] SUN Yu, WANG Shuohuan, LI Yukun, et al. ERNIE 2.0: a continual pre-training framework for language understanding[J]. Proceedings of the AAAI conference on artificial intelligence, 2020, 34(5): 8968–8975.
[7] 翟学明, 魏巍. 混合神经网络和条件随机场相结合的文本情感分析[J]. 智能系统学报, 2021, 16(2): 202–209
ZHAI Xueming, WEI Wei. Text sentiment analysis combining hybrid neural network and conditional random field[J]. CAAI transactions on intelligent systems, 2021, 16(2): 202–209
[8] ZHUANG Yin, LIU Zhen, LIU Tingting, et al. Implicit sentiment analysis based on multi-feature neural network model[J]. Soft computing, 2022, 26(2): 635–644.
[9] XIANG Chunli, REN Yafeng, JI Donghong. Identifying implicit polarity of events by using an attention-based neural network model[J]. IEEE access, 2019, 7: 133170–133177.
[10] 余本功, 王惠灵, 朱晓洁. 基于BG-DATT-CNN网络的方面级别情感分析[J]. 计算机工程与应用, 2022, 58(24): 151–157
YU Bengong, WANG Huiling, ZHU Xiaojie. Aspect-level sentiment analysis based on BG-DATT-CNN network[J]. Computer engineering and applications, 2022, 58(24): 151–157
[11] TAY Y, TUAN L A, HUI S C, et al. Reasoning with sarcasm by reading in-between[EB/OL]. (2018–05-08)[2022-09-13]. http://arxiv.org/abs/1805.02856.
[12] 卢欣, 李旸, 王素格. 融合语言特征的卷积神经网络的反讽识别方法[J]. 中文信息学报, 2019, 33(5): 31–38
LU Xin, LI Yang, WANG Suge. Linguistic features enhanced convolutional neural networks for irony recognition[J]. Journal of Chinese information processing, 2019, 33(5): 31–38
[13] MEHNDIRATTA P, SONI D. Identification of sarcasm using word embeddings and hyperparameters tuning[J]. Journal of discrete mathematical sciences and cryptography, 2019, 22(4): 465–489.
[14] 张庆林, 杜嘉晨, 徐睿峰. 基于对抗学习的讽刺识别研究[J]. 北京大学学报(自然科学版), 2019, 55(1): 29–36
ZHANG Qinglin, DU Jiachen, XU Ruifeng. Sarcasm detection based on adversarial learning[J]. Acta scientiarum naturalium universitatis pekinensis, 2019, 55(1): 29–36
[15] SHAH S, CHY A N. Fusion of hand-crafted features and deep semantic features in a unified neural model for irony detection in microblogs[C]//2020 IEEE 8th R10 Humanitarian Technology Conference. Kuching: IEEE, 2020: 1-6.
[16] 樊小超, 杨亮, 林鸿飞, 等. 基于多语义融合的反讽识别[J]. 中文信息学报, 2021, 35(6): 103–111
FAN Xiaochao, YANG Liang, LIN Hongfei, et al. Irony recognition based on multiple semantic fusion[J]. Journal of Chinese information processing, 2021, 35(6): 103–111
[17] PANDEY R, KUMAR A, SINGH J P, et al. Hybrid attention-based long short-term memory network for sarcasm identification[J]. Applied soft computing, 2021, 106: 107348.
[18] DONG Yuanfang, ZHANG Yitong, LI Jun. DC-BiGRU-CNN algorithm for irony recognition in Chinese social comments[J]. Mathematical problems in engineering, 2022, 2022: 5909033.
[19] 潘宏鹏, 汪东, 刘忠轶, 等. 考虑反讽语义识别的协同双向编码舆情评论情感分析研究[J]. 情报杂志, 2022, 41(5): 99–105,111
PAN Hongpeng, WANG Dong, LIU Zhongyi, et al. Public opinion comments sentiment analysis research considering ironic semantic recognition based on the collaborative BERT[J]. Journal of intelligence, 2022, 41(5): 99–105,111
[20] 张铭泉, 周辉, 曹锦纲. 基于注意力机制的双BERT有向情感文本分类研究[J]. 智能系统学报, 2022, 17(6): 1220–1227
ZHANG Mingquan, ZHOU Hui, CAO Jingang. Dual BERT directed sentiment text classification based on attention mechanism[J]. CAAI transactions on intelligent systems, 2022, 17(6): 1220–1227
[21] 曾碧卿, 韩旭丽, 王盛玉, 等. 层次化双注意力神经网络模型的情感分析研究[J]. 智能系统学报, 2020, 15(3): 460–467
ZENG Biqing, HAN Xuli, WANG Shengyu, et al. Hierarchical double-attention neural networks for sentiment classification[J]. CAAI transactions on intelligent systems, 2020, 15(3): 460–467
[22] PANDEY R, KUMAR A, SINGH JYOTI P, et al. Hybrid attention-based long short-term memory network for sarcasm identification[J]. Applied soft computing journal, 2021, 106: 107348.
[23] ZHU Dawei, ZHAN Qiusi, ZHOU Zhejian, et al. ConFiguRe: exploring discourse-level Chinese figures of speech[C]//Proceedings of the 29th International Conference on Computational Linguistics. Gyeongju: ACL, 2022: 3374-3385.
[24] HAO Y, ZHANG L, ZHENG Q, et al. A multi-scale sarcasm sentiment recognition algorithm incorporating sentence hierarchical representation[C]//Third International Conference on Artificial Intelligence and Computer Engineering. Dalian: SPIE, 2023, 12610: 666-675.
[25] LIU Lizhen, ZHANG Donghai, SONG Wei. Exploiting syntactic structures for humor recognition[C]//Proceedings of the 27th International Conference on Computational Linguistics. Santa Fe: ACL, 2018: 1875-1883.
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1900-01-01