{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T19:54:42Z","timestamp":1765828482025,"version":"3.41.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-00900-w","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T19:26:13Z","timestamp":1751397973000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Aspect Sentiment Triplet Extraction with Syntax-Semantics Graph Convolutional Network"],"prefix":"10.1007","volume":"18","author":[{"given":"Jingyun","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuwei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwei","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"900_CR1","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. 34. USA, New York, pp. 8600\u20138607 (2020)","DOI":"10.1609\/aaai.v34i05.6383"},{"key":"900_CR2","doi-asserted-by":"crossref","unstructured":"Li, R., Chen, H., Feng, F., Ma, Z., Wang, X., Hovy, E.: Dual graph convolutional networks 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. 6319\u20136329 (2021)","DOI":"10.18653\/v1\/2021.acl-long.494"},{"key":"900_CR3","unstructured":"Machado, M.T., Pardo, T.A.S.: Evaluating methods for extraction of aspect terms in opinion texts in Portuguese: the challenges of implicit aspects. In: The 13th Conference on Language Resources and Evaluation, France, Marseille, pp. 3819\u20133828 (2022)"},{"key":"900_CR4","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wen, Z., Zhao, Q., Yang, M., Xu, R.: Progressive self-training with discriminator for aspect term extraction. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 257\u2013268 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.23"},{"key":"900_CR5","doi-asserted-by":"crossref","unstructured":"Xu, H., Liu, B., Shu, L., Philip, S.Y.: Double embeddings and cnn-based sequence labeling for aspect extraction. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Australia, Melbourne, pp. 592\u2013598 (2018)","DOI":"10.18653\/v1\/P18-2094"},{"key":"900_CR6","doi-asserted-by":"crossref","unstructured":"Li, X., Lam, W.: Deep multi-task learning for aspect term extraction with memory interaction. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Denmark, Copenhagen, pp. 2886\u20132892 (2017)","DOI":"10.18653\/v1\/D17-1310"},{"key":"900_CR7","doi-asserted-by":"crossref","unstructured":"Feng, Y., Rao, Y., Tang, Y., Wang, N., Liu, H.: Target-specified sequence labeling with multi-head self-attention for target-oriented opinion words extraction. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1805\u20131815 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.145"},{"key":"900_CR8","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, F., Zhong, S.-H.: Training entire-space models for target-oriented opinion words extraction. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Spain, Madrid, pp. 1875\u20131879 (2022)","DOI":"10.1145\/3477495.3531768"},{"key":"900_CR9","doi-asserted-by":"crossref","unstructured":"Mensah, S., Sun, K., Aletras, N.: An empirical study on leveraging position embeddings for target-oriented opinion words extraction. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 9174\u20139179 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.722"},{"key":"900_CR10","doi-asserted-by":"crossref","unstructured":"Wang, B., Shen, T., Long, G., Zhou, T., Chang, Y.: Eliminating sentiment bias for aspect-level sentiment classification with unsupervised opinion extraction. In: Findings of the Association for Computational Linguistics: EMNLP 2021, Dominican Republic, Punta Cana, pp. 3002\u20133012 (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.258"},{"key":"900_CR11","doi-asserted-by":"crossref","unstructured":"Hou, X., Qi, P., Wang, G., Ying, R., Huang, J., He, X., Zhou, B.: Graph ensemble learning over multiple dependency trees for aspect-level sentiment classification. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2884\u20132894 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.229"},{"key":"900_CR12","doi-asserted-by":"crossref","unstructured":"Tian, Y., Chen, G., Song, Y.: Aspect-based sentiment analysis with type-aware graph convolutional networks and layer ensemble. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2910\u20132922 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.231"},{"key":"900_CR13","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Liao, L., Gao, Y., Jie, Z., Lu, W.: To be closer: learning to link up aspects with opinions. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3899\u20133909 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.317"},{"key":"900_CR14","doi-asserted-by":"crossref","unstructured":"Wang, S., Mazumder, S., Liu, B., Zhou, M., Chang, Y.: Target-sensitive memory networks for aspect sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Australia, Melbourne, pp. 957\u2013967 (2018)","DOI":"10.18653\/v1\/P18-1088"},{"key":"900_CR15","doi-asserted-by":"crossref","unstructured":"Wu, S., Fei, H., Ren, Y., Ji, D., Li, J.: Learn from syntax: improving pair-wise aspect and opinion terms extraction with rich syntactic knowledge. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) (2021)","DOI":"10.24963\/ijcai.2021\/545"},{"key":"900_CR16","unstructured":"Chakraborty, A.: Aspect and opinion term extraction using graph attention network. In: Proceedings of the 20th International Conference on Natural Language Processing (ICON), India, Goa, pp. 594\u2013602 (2023)"},{"key":"900_CR17","doi-asserted-by":"crossref","unstructured":"Gao, L., Wang, Y., Liu, T., Wang, J., Zhang, L., Liao, J.: Question-driven span labeling model for aspect\u2014opinion pair extraction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 12875\u201312883 (2021)","DOI":"10.1609\/aaai.v35i14.17523"},{"key":"900_CR18","doi-asserted-by":"crossref","unstructured":"Yu, G., Li, J., Luo, L., Meng, Y., Ao, X., He, Q.: Self question-answering: aspect-based sentiment analysis by role flipped machine reading comprehension. In: Findings of the Association for Computational Linguistics: EMNLP 2021, Dominican Republic, Punta Cana, pp. 1331\u20131342 (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.115"},{"key":"900_CR19","doi-asserted-by":"crossref","unstructured":"Li, H., Lu, W.: Learning latent sentiment scopes for entity-level sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.11016"},{"key":"900_CR20","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. USA, pp. 6714\u20136721 (2019)","DOI":"10.1609\/aaai.v33i01.33016714"},{"key":"900_CR21","doi-asserted-by":"crossref","unstructured":"Liu, J., Teng, Z., Cui, L., Liu, H., Zhang, Y.: Solving aspect category sentiment analysis as a text generation task. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 4406\u20134416 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.361"},{"key":"900_CR22","doi-asserted-by":"crossref","unstructured":"Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833\u2013843 (2020)","DOI":"10.18653\/v1\/2020.coling-main.72"},{"key":"900_CR23","doi-asserted-by":"crossref","unstructured":"Dai, J., Yan, H., Sun, T., Liu, P., Qiu, X.: Does syntax matter? A strong baseline for aspect-based sentiment analysis with RoBERTa. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1816\u20131829 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.146"},{"key":"900_CR24","doi-asserted-by":"crossref","unstructured":"Ye, H., Yan, Z., Luo, Z., Chao, W.: Dependency-tree based convolutional neural networks for aspect term extraction. In: Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23\u201326, 2017, Proceedings, Part II 21, South Korea, Jeju, pp. 350\u2013362 (2017)","DOI":"10.1007\/978-3-319-57529-2_28"},{"issue":"5","key":"900_CR25","doi-asserted-by":"publisher","first-page":"5833","DOI":"10.1007\/s11063-022-11115-x","volume":"55","author":"L Feng","year":"2023","unstructured":"Feng, L., Zeng, B., He, L., Xu, M., Deng, H., Chen, P., Huang, Z., Du, W.: Improving span-based aspect sentiment triplet extraction with abundant syntax knowledge. Neural Process. Lett. 55(5), 5833\u20135854 (2023)","journal-title":"Neural Process. Lett."},{"key":"900_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124854","volume":"256","author":"G Wang","year":"2024","unstructured":"Wang, G., Wang, B., Xu, F., Wang, R., Zhu, Z., Liu, P.: Domain-consistent syntactic representation for cross-domain aspect sentiment triplet extraction. Expert Syst. Appl. 256, 124854 (2024)","journal-title":"Expert Syst. Appl."},{"key":"900_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":"900_CR28","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"},{"issue":"2","key":"900_CR29","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1109\/TAFFC.2023.3291730","volume":"15","author":"L Yuan","year":"2023","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 (2023)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"900_CR30","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":"900_CR31","doi-asserted-by":"crossref","unstructured":"Chen, Y., Keming, C., Sun, X., Zhang, Z.: A span-level bidirectional network for aspect sentiment triplet extraction. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, United Arab Emirates, Abu Dhabi, pp. 4300\u20134309 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.289"},{"key":"900_CR32","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.neucom.2022.04.022","volume":"492","author":"Y Chen","year":"2022","unstructured":"Chen, Y., Zhang, Z., Zhou, G., Sun, X., Chen, K.: Span-based dual-decoder framework for aspect sentiment triplet extraction. Neurocomputing 492, 211\u2013221 (2022)","journal-title":"Neurocomputing"},{"key":"900_CR33","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":"900_CR34","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"},{"key":"900_CR35","doi-asserted-by":"crossref","unstructured":"Chen, S., Wang, Y., Liu, J., Wang, Y.: Bidirectional machine reading comprehension for aspect sentiment triplet extraction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 12666\u201312674 (2021)","DOI":"10.1609\/aaai.v35i14.17500"},{"key":"900_CR36","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), Ireland, Dublin, pp. 2974\u20132985 (2022)","DOI":"10.18653\/v1\/2022.acl-long.212"},{"key":"900_CR37","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the Conference on the North American Chapter of the Association for Computational Linguistics, Minnesota, Minneapolis, pp. 4171\u20134186 (2019)"},{"key":"900_CR38","unstructured":"Vaswani, A., Shazeer, N., Parmar, N.: Attention is all you need. In: Roceedings of the 31st International Conference on Neural Information Processing Systems, USA, California, pp. 6000\u20136010 (2017)"},{"key":"900_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhou, Z., Wang, Y.: SSEGCN: syntactic and semantic enhanced graph convolutional network for aspect-based sentiment analysis. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, United States, Seattle, pp. 4916\u20134925 (2022)","DOI":"10.18653\/v1\/2022.naacl-main.362"},{"key":"900_CR40","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks (2017)"},{"key":"900_CR41","doi-asserted-by":"crossref","unstructured":"Fan, Z., Wu, Z., Dai, X., Huang, S., Chen, J.: Target-oriented opinion words extraction with target-fused neural sequence labeling. 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), Minnesota, Minneapolis, pp. 2509\u20132518 (2019)","DOI":"10.18653\/v1\/N19-1259"},{"key":"900_CR42","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., Al-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De\u00a0Clercq, O.: SemEval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation, USA, San Diego, pp. 19\u201330 (2016)","DOI":"10.18653\/v1\/S16-1002"},{"key":"900_CR43","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., Al-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De\u00a0Clercq, O.: SemEval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation, USA, Denver, pp. 486\u2013495 (2015)","DOI":"10.18653\/v1\/S15-2082"},{"key":"900_CR44","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., Al-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De\u00a0Clercq, O.: SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation, Ireland, Dublin, pp. 27\u201335 (2014)","DOI":"10.3115\/v1\/S14-2004"},{"key":"900_CR45","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. 31. USA, California (2017)","DOI":"10.1609\/aaai.v31i1.10974"},{"key":"900_CR46","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, Italy, Florence, pp. 5268\u20135277 (2019)","DOI":"10.18653\/v1\/P19-1520"},{"key":"900_CR47","unstructured":"Loshchilov, I., Hutter, F.: Fixing weight decay regularization in ADAM. In: Proceedings of the Sixth International Conference on Learning Representations, Canada, Vancouver (2018)"},{"key":"900_CR48","doi-asserted-by":"crossref","unstructured":"Qi, P., Zhang, Y., Zhang, Y., Bolton, J., Manning, C.D.: Stanza: a python natural language processing toolkit for many human languages. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 101\u2013108 (2020)","DOI":"10.18653\/v1\/2020.acl-demos.14"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00900-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-00900-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00900-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T19:26:28Z","timestamp":1751397988000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-00900-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["900"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-00900-w","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,1]]},"assertion":[{"value":"29 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"167"}}