{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T12:12:45Z","timestamp":1769775165781,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556397","type":"print"},{"value":"9789819556403","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-5640-3_39","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T21:07:57Z","timestamp":1769720877000},"page":"615-630","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Complex Relationship Modeling Based Method for\u00a0Aspect Sentiment Triplet Extraction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4787-3858","authenticated-orcid":false,"given":"Yining","family":"Rao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"39_CR1","doi-asserted-by":"crossref","unstructured":"Chen, F., Yang, Z., Huang, Y.: A multi-task learning framework for end-to-end aspect sentiment triplet extraction. Neurocomputing, 12\u201321 (2022)","DOI":"10.1016\/j.neucom.2022.01.021"},{"key":"39_CR2","doi-asserted-by":"crossref","unstructured":"Chen, H., Zhai, Z., Feng, F., Li, R., Wang, X.: Enhanced multi-channel graph convolutional network for aspect sentiment triplet extraction. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2974\u20132985 (2022)","DOI":"10.18653\/v1\/2022.acl-long.212"},{"key":"39_CR3","doi-asserted-by":"crossref","unstructured":"Dai, H., Song, Y.: Neural aspect and opinion term extraction with mined rules as weak supervision. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5268\u20135277 (2019)","DOI":"10.18653\/v1\/P19-1520"},{"key":"39_CR4","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186 (2019)","DOI":"10.18653\/v1\/N19-1423"},{"key":"39_CR5","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":"39_CR6","unstructured":"Fei, H., Ren, Y., Zhang, Y., Ji, D.: Nonautoregressive encoder-decoder neural framework for end-to-end aspect-based sentiment triplet extraction. IEEE Trans. Neural Netw. Learn. Syst. (2021)"},{"key":"39_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126917","volume":"562","author":"B Jiang","year":"2023","unstructured":"Jiang, B., Liang, S., Liu, P., Dong, K., Li, H.: A semantically enhanced dual encoder for aspect sentiment triplet extraction. Neurocomputing 562, 126917 (2023)","journal-title":"Neurocomputing"},{"key":"39_CR8","doi-asserted-by":"crossref","unstructured":"Jing, H., Li, Z., Zhao, H., Jiang, S.: Seeking common but distinguishing difference, a joint aspect-based sentiment analysis model. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3910\u20133922 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.318"},{"key":"39_CR9","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, K., Han, D.: Duality-driven aspect sentiment triplet extraction with LLM and iterative reinforcement. Symmetry 17(5), 642 (2025), number: 5 Publisher: Multidisciplinary Digital Publishing Institute","DOI":"10.3390\/sym17050642"},{"key":"39_CR10","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, F., Zhong, S.h.: A more fine-grained aspect\u2013sentiment\u2013opinion triplet extraction task. Mathematics 11(14), 3165 (2023)","DOI":"10.3390\/math11143165"},{"issue":"1","key":"39_CR11","first-page":"1","volume":"5","author":"B Liu","year":"2012","unstructured":"Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1\u2013167 (2012)","journal-title":"Synth. Lect. Hum. Lang. Technol."},{"key":"39_CR12","doi-asserted-by":"crossref","unstructured":"Liu, P., Joty, S., Meng, H.: Fine-grained opinion mining with recurrent neural networks and word embeddings. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1433\u20131443 (2015)","DOI":"10.18653\/v1\/D15-1168"},{"key":"39_CR13","doi-asserted-by":"crossref","unstructured":"Mao, Y., Shen, Y., Yu, C., Cai, L.: A joint training dual-MRC framework for aspect based sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 13543\u201313551 (2021)","DOI":"10.1609\/aaai.v35i15.17597"},{"key":"39_CR14","doi-asserted-by":"crossref","unstructured":"Mukherjee, R., Nayak, T., Butala, Y., Bhattacharya, S., Goyal, P.: Paste: a tagging-free decoding framework using pointer networks for aspect sentiment triplet extraction. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 9279\u20139291 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.731"},{"key":"39_CR15","doi-asserted-by":"crossref","unstructured":"Peng, H., Xu, L., Bing, L., Huang, F., Lu, W., Si, L.: Knowing what, how and why: a near complete solution for aspect-based sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 8600\u20138607 (2020)","DOI":"10.1609\/aaai.v34i05.6383"},{"key":"39_CR16","doi-asserted-by":"crossref","unstructured":"Pontiki, M., et\u00a0al.: Semeval-2016 task 5: aspect based sentiment analysis. In: International Workshop on Semantic Evaluation, pp. 19\u201330 (2016)","DOI":"10.18653\/v1\/S16-1002"},{"key":"39_CR17","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486\u2013495 (2015)","DOI":"10.18653\/v1\/S15-2082"},{"key":"39_CR18","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Papageorgiou, H., Galanis, D., Androutsopoulos, I., Pavlopoulos, J., Manandhar, S.: Semeval-2014 task 4: aspect based sentiment analysis. In: SemEval 2014, p. 27 (2014)","DOI":"10.3115\/v1\/S14-2004"},{"key":"39_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109625","volume":"139","author":"X Sun","year":"2025","unstructured":"Sun, X., Qi, J., Zhu, Z., Li, M., Pei, H., Meng, J.: Senticnet and abstract meaning representation driven attention-gate semantic framework for aspect sentiment triplet extraction. Eng. Appl. Artif. Intell. 139, 109625 (2025)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"39_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2025.100506","volume":"39","author":"M Tang","year":"2025","unstructured":"Tang, M., Yang, K., Tao, L., Zhao, M., Zhou, W.: Multi-granularity enhanced graph convolutional network for aspect sentiment triplet extraction. Big Data Res. 39, 100506 (2025)","journal-title":"Big Data Res."},{"key":"39_CR21","doi-asserted-by":"crossref","unstructured":"Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Recursive neural conditional random fields for aspect-based sentiment analysis. arXiv preprint arXiv:1603.06679 (2016)","DOI":"10.18653\/v1\/D16-1059"},{"key":"39_CR22","doi-asserted-by":"crossref","unstructured":"Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a031 (2017)","DOI":"10.1609\/aaai.v31i1.10974"},{"key":"39_CR23","doi-asserted-by":"crossref","unstructured":"Wu, Z., Ying, C., Zhao, F., Fan, Z., Dai, X., Xia, R.: Grid tagging scheme for aspect-oriented fine-grained opinion extraction. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 2576\u20132585 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.234"},{"key":"39_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128064","volume":"597","author":"T Xia","year":"2024","unstructured":"Xia, T., Sun, X., Yang, Y., Long, Y., Sutcliffe, R.: A dual relation-encoder network for aspect sentiment triplet extraction. Neurocomputing 597, 128064 (2024)","journal-title":"Neurocomputing"},{"key":"39_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112342","volume":"301","author":"H Xu","year":"2024","unstructured":"Xu, H., Tang, M., Cai, T., Hu, J., Zhao, M.: Dual-enhanced generative model with graph attention network and contrastive learning for aspect sentiment triplet extraction. Knowl.-Based Syst. 301, 112342 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"39_CR26","doi-asserted-by":"crossref","unstructured":"Xu, L., Chia, Y.K., Bing, L.: Learning span-level interactions for aspect sentiment triplet extraction. In: 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), pp. 4755\u20134766 (2021)","DOI":"10.18653\/v1\/2021.acl-long.367"},{"key":"39_CR27","doi-asserted-by":"crossref","unstructured":"Xu, L., Li, H., Lu, W., Bing, L.: Position-aware tagging for aspect sentiment triplet extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2339\u20132349 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.183"},{"key":"39_CR28","doi-asserted-by":"crossref","unstructured":"Yan, H., Dai, J., Ji, T., Qiu, X., Zhang, Z.: A unified generative framework for aspect-based sentiment analysis. In: 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), pp. 2416\u20132429 (2021)","DOI":"10.18653\/v1\/2021.acl-long.188"},{"issue":"2","key":"39_CR29","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1109\/TAFFC.2023.3291730","volume":"15","author":"L Yuan","year":"2024","unstructured":"Yuan, L., Wang, J., Yu, L.C., Zhang, X.: Encoding syntactic information into transformers for aspect-based sentiment triplet extraction. IEEE Trans. Affect. Comput. 15(2), 722\u2013735 (2024)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"39_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, C., Li, Q., Song, D., Wang, B.: A multi-task learning framework for opinion triplet extraction. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 819\u2013828 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.72"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5640-3_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T21:08:05Z","timestamp":1769720885000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5640-3_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556397","9789819556403"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5640-3_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/apweb2025.sau.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}