{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:28:19Z","timestamp":1742945299352,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":43,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819962068"},{"type":"electronic","value":"9789819962075"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-6207-5_23","type":"book-chapter","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T08:45:34Z","timestamp":1695113134000},"page":"367-381","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Learnable Conjunction Enhanced Model for Chinese Sentiment Analysis"],"prefix":"10.1007","author":[{"given":"Bingfei","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongying","family":"Zan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajia","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjie","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452\u2013461 (2017)","DOI":"10.18653\/v1\/D17-1047"},{"issue":"01","key":"23_CR2","first-page":"133","volume":"33","author":"Y Cheng","year":"2019","unstructured":"Cheng, Y., Ye, Z., Wang, M., Zhang, Q., Zhang, G.: Chinese text sentiment orientation analysis based on convolution neural network and hierarchical attention network. J. Chin. Inf. Process. 33(01), 133\u2013142 (2019)","journal-title":"J. Chin. Inf. Process."},{"key":"23_CR3","doi-asserted-by":"publisher","first-page":"3504","DOI":"10.1109\/TASLP.2021.3124365","volume":"29","author":"Y Cui","year":"2021","unstructured":"Cui, Y., Che, W., Liu, T., Qin, B., Yang, Z.: Pre-training with whole word masking for Chinese BERT. IEEE\/ACM Trans. Audio Speech Lang. Process. 29, 3504\u20133514 (2021). https:\/\/doi.org\/10.1109\/TASLP.2021.3124365","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433\u20133442 (2018)","DOI":"10.18653\/v1\/D18-1380"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5","key":"23_CR6","first-page":"189","volume":"37","author":"Y He","year":"2018","unstructured":"He, Y., Zhao, S., He, L.: Micro-text emotional tendentious classification based on combination of emotion knowledge and machine-learning algorithm. J. Intell 37(5), 189\u2013194 (2018)","journal-title":"J. Intell"},{"key":"23_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/978-3-319-93372-6_22","volume-title":"Social, Cultural, and Behavioral Modeling","author":"B Huang","year":"2018","unstructured":"Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds.) SBP-BRiMS 2018. LNCS, vol. 10899, pp. 197\u2013206. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93372-6_22"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Johnson, R., Zhang, T.: Deep pyramid convolutional neural networks for text categorization. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 562\u2013570 (2017)","DOI":"10.18653\/v1\/P17-1052"},{"key":"23_CR9","doi-asserted-by":"publisher","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746\u20131751. Association for Computational Linguistics, Doha, Qatar, October 2014. https:\/\/doi.org\/10.3115\/v1\/D14-1181, https:\/\/aclanthology.org\/D14-1181","DOI":"10.3115\/v1\/D14-1181"},{"key":"23_CR10","unstructured":"Kunli, Z., Hongying, Z., Yumei, C., Yingjie, H., Dan, Z.: Construction and application of the Chinese function word usage knowledge base (2013)"},{"key":"23_CR11","doi-asserted-by":"publisher","unstructured":"Li, X., Yan, H., Qiu, X., Huang, X.: FLAT: Chinese NER using flat-lattice transformer. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6836\u20136842. Association for Computational Linguistics, Online, July 2020. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.611, https:\/\/aclanthology.org\/2020.acl-main.611","DOI":"10.18653\/v1\/2020.acl-main.611"},{"key":"23_CR12","doi-asserted-by":"publisher","unstructured":"Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 946\u2013956. Association for Computational Linguistics, Melbourne, Australia, July 2018. https:\/\/doi.org\/10.18653\/v1\/P18-1087, https:\/\/aclanthology.org\/P18-1087","DOI":"10.18653\/v1\/P18-1087"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Li, X., Bing, L., Li, P., Lam, W.: A unified model for opinion target extraction and target sentiment prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6714\u20136721 (2019)","DOI":"10.1609\/aaai.v33i01.33016714"},{"issue":"8","key":"23_CR14","first-page":"2301","volume":"38","author":"Z Li","year":"2021","unstructured":"Li, Z., Chen, L., Zhang, S.: Chinese text sentiment analysis based on ELMo and Bi-SAN. Appl. Res. Comput. 38(8), 2301\u20132307 (2021)","journal-title":"Appl. Res. Comput."},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Liu, F., Gao, M., Liu, Y., Lei, K.: Self-adaptive scaling approach for learnable residual structure. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pp. 862\u2013870 (2019)","DOI":"10.18653\/v1\/K19-1080"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Liu, F., Ren, X., Zhang, Z., Sun, X., Zou, Y.: Rethinking skip connection with layer normalization in transformers and ResNets. arXiv preprint arXiv:2105.07205 (2021)","DOI":"10.18653\/v1\/2020.coling-main.320"},{"key":"23_CR17","doi-asserted-by":"publisher","unstructured":"Liu, Q.: Study of conjunctive scope and its \u201cspecial category\u201d in modern Chinese. J. Jiangsu Normal Univ. Philos. Soc. Sci. Ed. 42, 85\u201390 (2016). https:\/\/doi.org\/10.16095\/j.cnki.cn32-1833\/c.2016.01.014","DOI":"10.16095\/j.cnki.cn32-1833\/c.2016.01.014"},{"key":"23_CR18","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"23_CR19","first-page":"57","volume":"52","author":"Y Liu","year":"2015","unstructured":"Liu, Y., Ju, S., Wu, S., Su, C.: Classification of Chinese texts sentiment based on semantic and conjunction. J. Sichuan Univ. Nat. Sci. Ed. 52, 57\u201362 (2015)","journal-title":"J. Sichuan Univ. Nat. Sci. Ed."},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. AAAI Press (2017)","DOI":"10.24963\/ijcai.2017\/568"},{"key":"23_CR21","doi-asserted-by":"publisher","unstructured":"Majumder, N., Bhardwaj, R., Poria, S., Gelbukh, A., Hussain, A.: Improving aspect-level sentiment analysis with aspect extraction. Neural Comput. Appl. 34(11), 8333\u20138343 (2022). https:\/\/doi.org\/10.1007\/s00521-020-05287-7","DOI":"10.1007\/s00521-020-05287-7"},{"key":"23_CR22","doi-asserted-by":"publisher","unstructured":"Mengge, X., Yu, B., Liu, T., Zhang, Y., Meng, E., Wang, B.: Porous lattice transformer encoder for Chinese NER. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 3831\u20133841. International Committee on Computational Linguistics, Barcelona, Spain (Online), December 2020. https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.340, https:\/\/aclanthology.org\/2020.coling-main.340","DOI":"10.18653\/v1\/2020.coling-main.340"},{"key":"23_CR23","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.knosys.2018.02.034","volume":"148","author":"H Peng","year":"2018","unstructured":"Peng, H., Ma, Y., Li, Y., Cambria, E.: Learning multi-grained aspect target sequence for Chinese sentiment analysis. Knowl.-Based Syst. 148, 167\u2013176 (2018)","journal-title":"Knowl.-Based Syst."},{"key":"23_CR24","doi-asserted-by":"publisher","unstructured":"Peters, M.E., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 2227\u20132237. Association for Computational Linguistics, New Orleans, Louisiana, June 2018. https:\/\/doi.org\/10.18653\/v1\/N18-1202, https:\/\/aclanthology.org\/N18-1202","DOI":"10.18653\/v1\/N18-1202"},{"key":"23_CR25","doi-asserted-by":"publisher","unstructured":"Pontiki, M., et al.: SemEval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 19\u201330. Association for Computational Linguistics, San Diego, California, June 2016. https:\/\/doi.org\/10.18653\/v1\/S16-1002, https:\/\/aclanthology.org\/S16-1002","DOI":"10.18653\/v1\/S16-1002"},{"key":"23_CR26","doi-asserted-by":"publisher","unstructured":"Shaw, P., Uszkoreit, J., Vaswani, A.: Self-attention with relative position representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 2 (Short Papers), pp. 464\u2013468. Association for Computational Linguistics, New Orleans, Louisiana, June 2018. https:\/\/doi.org\/10.18653\/v1\/N18-2074, https:\/\/aclanthology.org\/N18-2074","DOI":"10.18653\/v1\/N18-2074"},{"key":"23_CR27","doi-asserted-by":"publisher","unstructured":"Wei, S., Fu, Y.: Microblog short text mining considering context: a method of sentiment analysis. Comput. Sci. 48(6A), 158 (2021). https:\/\/doi.org\/10.11896\/jsjkx.210200089, https:\/\/www.jsjkx.com\/EN\/abstract\/article_19978.shtml","DOI":"10.11896\/jsjkx.210200089"},{"key":"23_CR28","unstructured":"Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. arXiv preprint arXiv:1505.00387 (2015)"},{"key":"23_CR29","doi-asserted-by":"publisher","unstructured":"Sun, C., Huang, L., Qiu, X.: Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 380\u2013385. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019. https:\/\/doi.org\/10.18653\/v1\/N19-1035, https:\/\/aclanthology.org\/N19-1035","DOI":"10.18653\/v1\/N19-1035"},{"key":"23_CR30","unstructured":"Sun, Y., et al.: ERNIE: enhanced representation through knowledge integration. arXiv preprint arXiv:1904.09223 (2019)"},{"key":"23_CR31","unstructured":"Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298\u20133307. The COLING 2016 Organizing Committee, Osaka, Japan, December 2016. https:\/\/aclanthology.org\/C16-1311"},{"key":"23_CR32","doi-asserted-by":"publisher","unstructured":"Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 214\u2013224. Association for Computational Linguistics, Austin, Texas, November 2016. https:\/\/doi.org\/10.18653\/v1\/D16-1021, https:\/\/aclanthology.org\/D16-1021","DOI":"10.18653\/v1\/D16-1021"},{"key":"23_CR33","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"23_CR34","doi-asserted-by":"crossref","unstructured":"Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606\u2013615 (2016)","DOI":"10.18653\/v1\/D16-1058"},{"issue":"4","key":"23_CR35","first-page":"642","volume":"35","author":"R Xie","year":"2020","unstructured":"Xie, R., Li, Y.: Text sentiment classification model based on BERT and dual channel attention. J. Data Acquisit. Process. 35(4), 642\u2013652 (2020)","journal-title":"J. Data Acquisit. Process."},{"issue":"6","key":"23_CR36","first-page":"95","volume":"21","author":"J Xu","year":"2007","unstructured":"Xu, J., Ding, Y.X., Wang, X.L.: Sentiment classification for Chinese news using machine learning methods. J. Chin. Inf. Process. 21(6), 95\u2013100 (2007)","journal-title":"J. Chin. Inf. Process."},{"key":"23_CR37","unstructured":"Yan, H., Deng, B., Li, X., Qiu, X.: TENER: adapting transformer encoder for named entity recognition. arXiv preprint arXiv:1911.04474 (2019)"},{"issue":"9","key":"23_CR38","first-page":"225","volume":"28","author":"J Yang","year":"2011","unstructured":"Yang, J., Lin, S.: Emotion analysis on text words and sentences based on SVM. Jisuanji Yingyong yu Ruanjian 28(9), 225\u2013228 (2011)","journal-title":"Jisuanji Yingyong yu Ruanjian"},{"issue":"4","key":"23_CR39","first-page":"185","volume":"21","author":"H Zan","year":"2011","unstructured":"Zan, H., Zhang, K., Zhu, X., Yu, S.: Research on the Chinese function word usage knowledge base. Int. J. Asian Lang. Process. 21(4), 185\u2013198 (2011)","journal-title":"Int. J. Asian Lang. Process."},{"key":"23_CR40","unstructured":"Zhang, D., Wang, D.: Relation classification via recurrent neural network. arXiv preprint arXiv:1508.01006 (2015)"},{"issue":"3","key":"23_CR41","first-page":"1","volume":"29","author":"K Zhang","year":"2015","unstructured":"Zhang, K., Zan, H., Chai, Y., Han, Y., Zhao, D.: Survey of the Chinese function word usage knowledge base. J. Chin. Inf. Process. 29(3), 1\u20138 (2015)","journal-title":"J. Chin. Inf. Process."},{"key":"23_CR42","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1016\/j.neucom.2015.09.066","volume":"173","author":"S Zhao","year":"2016","unstructured":"Zhao, S., Liu, T., Zhao, S., Chen, Y., Nie, J.Y.: Event causality extraction based on connectives analysis. Neurocomputing 173, 1943\u20131950 (2016)","journal-title":"Neurocomputing"},{"key":"23_CR43","unstructured":"Zhu, Y.L., Min, J., Zhou, Y.Q., Huang, X.J., Wu, L.D.: Semantic orientation computing based on HowNet. J. Chin. Inf. Process. 20(1), 14\u201320 (2006)"}],"container-title":["Lecture Notes in Computer Science","Chinese Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-6207-5_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T08:51:09Z","timestamp":1695113469000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-6207-5_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819962068","9789819962075"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-6207-5_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"20 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China National Conference on Chinese Computational Linguistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Harbin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncl2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}