1 |
张严,李天瑞.面向评论的方面级情感分析综述[J].计算机科学,2020,47(6):194-200. 10.11896/jsjkx.200200127
|
|
ZHANG Y, LI T R. Review of comment-oriented aspect-level sentiment analysis[J]. Computer Science, 2020, 47(6):194-200. 10.11896/jsjkx.200200127
|
2 |
史伟,付月. 考虑语境的微博短文本挖掘:情感分析的方法[J]. 计算机科学, 2021, 48(6A):158-164. 10.11896/jsjkx.210200089
|
|
SHI W, FU Y. Microblog short text mining considering context: a method of sentiment analysis [J]. Computer Science, 2021, 48(6A):158-164. 10.11896/jsjkx.210200089
|
3 |
AKOURY N, KRISHNA K, IYYER M. Syntactically supervised transformers for faster neural machine translation [C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: ACL, 2019: 1269-1281. 10.18653/v1/p19-1122
|
4 |
PHAN M H, OGUNBONA P O. Modelling context and syntactical features for aspect-based sentiment analysis [C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: ACL, 2020: 3211-3220. 10.18653/v1/2020.acl-main.293
|
5 |
WANG Y Q, HUANG M L, ZHU X Y, et al. Attention-based LSTM for aspect-level sentiment classification [C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2016: 606-615. 10.18653/v1/d16-1058
|
6 |
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2017: 6000-6010.
|
7 |
MA D H, LI S J, ZHANG X D, et al. Interactive attention networks for aspect-level sentiment classification[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence. California: ijcai.org, 2017: 4068-4074. 10.24963/ijcai.2017/568
|
8 |
LI X, BING L D, LAM W, et al. Transformation networks for target-oriented sentiment classification [C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA: ACL, 2018: 946-956. 10.18653/v1/p18-1087
|
9 |
ZENG B Q, YANG H, XU R Y, et al. LCF: a local context focus mechanism for aspect-based sentiment classification[J]. Applied Sciences, 2019, 9(16): No.3389. 10.3390/app9163389
|
10 |
HUANG B X, CARLEY K M. Syntax-aware aspect level sentiment classification with graph attention networks[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg, PA: ACL, 2019: 5469-5477. 10.18653/v1/d19-1549
|
11 |
YAN H, DAI J Q, JI T, et al. A unified generative framework for aspect-based sentiment analysis[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA: ACL, 2021: 2416-2429. 10.18653/v1/2021.acl-long.188
|
12 |
MA L H, RABBANY R, ROMERO-SORIANO A. Graph attention networks with positional embeddings [C]// Proceedings of the 2021 Pacific-Asia Conference on Knowledge Discovery and Data Mining, LNCS 12712. Cham: Springer, 2021: 514-527.
|
13 |
BAI X F, LIU P B, ZHANG Y. Investigating typed syntactic dependencies for targeted sentiment classification using graph attention neural network[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 29: 503-514. 10.1109/taslp.2020.3042009
|
14 |
HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. 10.1162/neco.1997.9.8.1735
|
15 |
CHO K, van MERRIËNBOER B, GU̇LÇEHRE Ç, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2014: 1724-1734. 10.3115/v1/d14-1179
|
16 |
JOZEFOWICZ R, ZAREMBA W, SUTSKEVER I. An empirical exploration of recurrent network architectures [C]// Proceedings of the 32nd International Conference on Machine Learning. New York: JMLR.org, 2015: 2342-2350.
|
17 |
DOZAT T, MANNING C D. Deep biaffine attention for neural dependency parsing[EB/OL]. (2017-03-10) [2022-06-19].. 10.18653/v1/k17-3002
|
18 |
FAN F F, FENG Y S, ZHAO D Y. Multi-grained attention network for aspect-level sentiment classification [C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2018: 3433-3442. 10.18653/v1/d18-1380
|
19 |
SONG Y W, WANG J H, JIANG T, et al. Attentional encoder network for targeted sentiment classification [EB/OL]. (2019-04-01) [2022-06-19].. 10.1007/978-3-030-30490-4_9
|
20 |
JIANG Q N, CHEN L, XU R F, et al. A challenge dataset and effective models for aspect-based sentiment analysis[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg, PA: ACL, 2019: 6280-6285. 10.18653/v1/d19-1654
|
21 |
SABOUR S, FROSST N, HINTON G E. Dynamic routing between capsules [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2017: 3859-3869.
|
22 |
NGUYEN T H, SHIRAI K. PhraseRNN: phrase recursive neural network for aspect-based sentiment analysis[C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2015: 2509-2514. 10.18653/v1/d15-1298
|
23 |
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 (Volume 2: Short Papers). Stroudsburg, PA: ACL, 2014: 49-54. 10.3115/v1/p14-2009
|
24 |
NGUYEN H T, LE NGUYEN M. Effective attention networks for aspect-level sentiment classification [C]// Proceedings of the 10th International Conference on Knowledge and Systems Engineering. Piscataway: IEEE, 2018: 25-30. 10.1109/kse.2018.8573324
|
25 |
SUN K, ZHANG R C, MENSAH S, et al. Aspect-level sentiment analysis via convolution over dependency tree [C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg, PA: ACL, 2019: 5679-5688. 10.18653/v1/d19-1569
|
26 |
WANG K, SHEN W Z, YANG Y Y, et al. Relational graph attention network for aspect-based sentiment analysis[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: ACL, 2020: 3229-3238. 10.18653/v1/2020.acl-main.295
|
27 |
TIAN Y H, CHEN G M, SONG Y. Aspect-based sentiment analysis with type-aware graph convolutional networks and layer ensemble[C]// Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA: ACL, 2021: 2910-2922. 10.18653/v1/2021.naacl-main.231
|