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                     tuning strategy for low-resource domains. ACM Trans. on Asian and Low-resource Language Information Processing, 2024, 23(4): 59.
                     [doi: 10.1145/3653299]
                 [26]   Zhou CZ, Song DD, Tian YH, Wu ZJ, Wang H, Zhang XY, Yang J, Yang ZY, Zhang SH. A comprehensive evaluation of large language
                     models on aspect-based sentiment analysis. arXiv:2412.02279, 2024.
                 [27]   Mao  DZ,  Li  HY,  Shao  YQ.  Semi-supervised  domain  adaptation  for  semantic  dependency  parsing.  Journal  of  Chinese  Information
                     Processing, 2022, 36(2): 22–28 (in Chinese with English abstract). [doi: 10.3969/j.issn.1003-0077.2022.02.003]
                 [28]   Wu  FZ,  Huang  YF,  Yan  J.  Active  sentiment  domain  adaptation.  In:  Proc.  of  the  55th  Annual  Meeting  of  the  Association  for
                     Computational Linguistics (Vol. 1: Long Papers). Vancouver: ACL, 2017. 1701–1711. [doi: 10.18653/v1/P17-1156]
                 [29]   Wu  HR,  Wu  QY,  Ng  MK.  Knowledge  preserving  and  distribution  alignment  for  heterogeneous  domain  adaptation.  ACM  Trans.  on
                     Information Systems, 2022, 40(1): 16. [doi: 10.1145/3469856]
                 [30]   Ping  R,  Zhou  SS,  Li  D.  Cost  sensitive  random  forest  classification  algorithm  for  highly  unbalanced  data.  Pattern  Recognition  and
                     Artificial Intelligence, 2020, 33(3): 249–257 (in Chinese with English abstract). [doi: 10.16451/j.cnki.issn1003-6059.202003006]
                 [31]   Li JC, Li GB, Shi YM, Yu YZ. Cross-domain adaptive clustering for semi-supervised domain adaptation. In: Proc. of the 2021 IEEE/CVF
                     Conf. on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021. 2505–2514. [doi: 10.1109/CVPR46437.2021.00253]
                 [32]   Saito K, Ushiku Y, Harada T. Asymmetric tri-training for unsupervised domain adaptation. In: Proc. of the 34th Int’l Conf. on Machine
                     Learning. Sydney: PMLR, 2017. 2988–2997.
                 [33]   Rotman  G,  Reichart  R.  Deep  contextualized  self-training  for  low  resource  dependency  parsing.  Trans.  of  the  Association  for
                     Computational Linguistics, 2019, 7: 695–713. [doi: 10.1162/tacl_a_00294]
                 [34]   Yu JF, Zhao QK, Xia R. Cross-domain data augmentation with domain-adaptive language modeling for aspect-based sentiment analysis.
                     In:  Proc.  of  the  61st  Annual  Meeting  of  the  Association  for  Computational  Linguistics  (Vol.  1:  Long  Papers).  Toronto:  ACL,  2023.
                     1456–1470. [doi: 10.18653/v1/2023.acl-long.81]
                 [35]   Li  Z,  Li  X,  Wei  Y,  Bing  LD,  Zhang  Y,  Yang  Q.  Transferable  end-to-end  aspect-based  sentiment  analysis  with  selective  adversarial
                     learning. In: Proc. of the 2019 Conf. on Empirical Methods in Natural Language Processing and the 9th Int’l Joint Conf. on Natural
                     Language Processing. Hong Kong: ACL, 2019. 4590–4600. [doi: 10.18653/v1/D19-1466]
                 [36]   Zhou Y, Zhu FQ, Song P, Han JZ, Guo T, Hu SL. An adaptive hybrid framework for cross-domain aspect-based sentiment analysis. In:
                     Proc. of the 35th AAAI Conf. on Artificial Intelligence. AAAI Press, 2021. 14630–14637. [doi: 10.1609/aaai.v35i16.17719]
                 [37]   He JZ, Jia X, Chen SJ, Liu JZ. Multi-source domain adaptation with collaborative learning for semantic segmentation. In: Proc. of the
                     2021 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021. 11003–11012. [doi: 10.1109/CVPR46437.
                     2021.01086]
                 [38]   Dai Y, Liu J, Zhang J, Fu HG, Xu ZL. Unsupervised sentiment analysis by transferring multi-source knowledge. Cognitive Computation,
                     2021, 13(5): 1185–1197. [doi: 10.1007/s12559-020-09792-8]
                 [39]   Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S. SemEval-2014 task 4: Aspect based sentiment
                     analysis. In: Proc. of the 8th Int’l Workshop on Semantic Evaluation. Dublin: ACL, 2014. 27–35. [doi: 10.3115/v1/S14-2004]
                 [40]   Pontiki M, Galanis D, Papageorgiou H, Manandhar S, Androutsopoulos I. SemEval-2015 task 12: Aspect based sentiment analysis. In:
                     Proc. of the 9th Int’l Workshop on Semantic Evaluation. Denver: ACL, 2015. 486–495. [doi: 10.18653/v1/S15-2082]
                 [41]   Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, Al-Smadi M, Al-Ayyoub M, Zhao AA, Qin B, de Clercq O,
                     Hoste V, Apidianaki M, Tannier X, Loukachevitch N, Kotelnikov E, Bel N, Jiménez-Zafra SM, Eryiğit G. SemEval-2016 task 5: Aspect
                     based sentiment analysis. In: Proc. of the 10th Int’l Workshop on Semantic Evaluation. San Diego: ACL, 2016. 19–30. [doi: 10.18653/v1/
                     S16-1002]
                 [42]   Hu MQ, Liu B. Mining and summarizing customer reviews. In: Proc. of the 10th ACM SIGKDD Int’l Conf. on Knowledge Discovery
                     and Data Mining. Seattle: ACM, 2004. 168–177. [doi: 10.1145/1014052.1014073]
                 [43]   Toprak C, Jakob N, Gurevych I. Sentence and expression level annotation of opinions in user-generated discourse. In: Proc. of the 48th
                     Annual Meeting of the Association for Computational Linguistics. Uppsala: ACL, 2010. 575–584.
                 [44]   Saeidi M, Bouchard G, Liakata M, Riedel S. SentiHood: Targeted aspect based sentiment analysis dataset for urban neighbourhoods. In:
                     Proc. of the 26th Int’l Conf. on Computational Linguistics: Technical Papers. Osaka: ACL, 2016. 1546–1556.
                 [45]   Du CS, Huang L. Text classification research with attention-based recurrent neural networks. Int’l Journal of Computers Communications
                     & Control, 2018, 13(1): 50–61. [doi: 10.15837/ijccc.2018.1.3142]
                 [46]   Bin Y, Yang Y, Shen FM, Xu X, Shen HT. Bidirectional long-short term memory for video description. In: Proc. of the 24th ACM Int’l
                     Conf. on Multimedia. Amsterdam: ACM, 2016. 436–440. [doi: 10.1145/2964284.2967258]
                 [47]   Wang YQ, Huang ML, Zhu XY, Zhao L. Attention-based LSTM for aspect-level sentiment classification. In: Proc. of the 2016 Conf. on
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