{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T07:48:21Z","timestamp":1743148101974,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031251979"},{"type":"electronic","value":"9783031251986"}],"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\/978-3-031-25198-6_8","type":"book-chapter","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T07:32:38Z","timestamp":1676014358000},"page":"101-113","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AOPSS: A Joint Learning Framework for\u00a0Aspect-Opinion Pair Extraction as\u00a0Semantic Segmentation"],"prefix":"10.1007","author":[{"given":"Chengwei","family":"Wang","sequence":"first","affiliation":[]},{"given":"Tao","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lin","family":"Yue","sequence":"additional","affiliation":[]},{"given":"Lu","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,10]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Chen, S., Liu, J., Wang, Y., Zhang, W., Chi, Z.: Synchronous double-channel recurrent network for aspect-opinion pair extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6515\u20136524 (2020)","DOI":"10.18653\/v1\/2020.acl-main.582"},{"key":"8_CR2","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":"8_CR3","doi-asserted-by":"crossref","unstructured":"Fan, Z., Wu, Z., Dai, X.Y., 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, pp. 2509\u20132518 (2019)","DOI":"10.18653\/v1\/N19-1259"},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Gao, L., Wang, Y., Liu, T., Wang, J., Zhang, L., Liao, J.: Question-driven span labeling model for aspect-opinion pair extraction. In: Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, pp. 12875\u201312883 (2021)","DOI":"10.1609\/aaai.v35i14.17523"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168\u2013177 (2004)","DOI":"10.1145\/1014052.1014073"},{"key":"8_CR6","unstructured":"Klinger, R., Cimiano, P.: Bi-directional inter-dependencies of subjective expressions and targets and their value for a joint model. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 848\u2013854 (2013)"},{"key":"8_CR7","unstructured":"Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 282\u2013289 (2001)"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Liu, Q., Chen, B., Lou, J.G., Zhou, B., Zhang, D.: Incomplete utterance rewriting as semantic segmentation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 2846\u20132857 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.227"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Pontiki, M., et al.: SemEval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation, pp. 19\u201330 (2016)","DOI":"10.18653\/v1\/S16-1002"},{"key":"8_CR10","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, pp. 486\u2013495 (2015)","DOI":"10.18653\/v1\/S15-2082"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation, pp. 27\u201335 (2014)","DOI":"10.3115\/v1\/S14-2004"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Popescu, A.M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pp. 339\u2013346 (2005)","DOI":"10.3115\/1220575.1220618"},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"8_CR14","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 Thirty-First AAAI Conference on Artificial Intelligence, pp. 3316\u20133322 (2017)","DOI":"10.1609\/aaai.v31i1.10974"},{"issue":"4","key":"8_CR15","first-page":"943","volume":"50","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Chen, W., Pi, D., Yue, L.: Adaptive multi-hop reading on memory neural network with selective coverage mechanism for medication recommendation. Acta Electron. Sin. 50(4), 943\u2013953 (2022)","journal-title":"Acta Electron. Sin."},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, W., Pi, D., Yue, L., Xu, M., Li, X.: Multi-Hop Reading on Memory Neural Network with Selective Coverage for Medication Recommendation, In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 2020\u20132029 (2021)","DOI":"10.1145\/3459637.3482278"},{"key":"8_CR17","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, pp. 3957\u20133963 (2021)","DOI":"10.24963\/ijcai.2021\/545"},{"key":"8_CR18","doi-asserted-by":"publisher","first-page":"2396","DOI":"10.1109\/TASLP.2021.3095672","volume":"29","author":"S Wu","year":"2021","unstructured":"Wu, S., Fei, H., Ren, Y., Li, B., Li, F., Ji, D.: High-order pair-wise aspect and opinion terms extraction with edge-enhanced syntactic graph convolution. IEEE\/ACM Trans. Audio Speech Lang. Process. 29, 2396\u20132406 (2021)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"8_CR19","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":"8_CR20","doi-asserted-by":"crossref","unstructured":"Xu, H., Liu, B., Shu, L., Yu, P.S.: Double embeddings and CNN-based sequence labeling for aspect extraction. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 592\u2013598 (2018)","DOI":"10.18653\/v1\/P18-2094"},{"key":"8_CR21","unstructured":"Yang, B., Cardie, C.: Joint inference for fine-grained opinion extraction. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 1640\u20131649 (2013)"},{"key":"8_CR22","unstructured":"Yin, Y., Wei, F., Dong, L., Xu, K., Zhang, M., Zhou, M.: Unsupervised word and dependency path embeddings for aspect term extraction. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 2979\u20132985 (2016)"},{"key":"8_CR23","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/j.neucom.2016.12.084","volume":"253","author":"L Yue","year":"2017","unstructured":"Yue, L., Shi, Z., Han, J., Wang, S., Chen, W., Zuo, W.: Multi-factors based sentence ordering for cross-document fusion from multimodal content. Neurocomputing 253, 6\u201314 (2017)","journal-title":"Neurocomputing"},{"issue":"5","key":"8_CR24","doi-asserted-by":"publisher","first-page":"2715","DOI":"10.1007\/s11280-019-00764-z","volume":"23","author":"L Yue","year":"2020","unstructured":"Yue, L., Tian, D., Chen, W., Han, X., Yin, M.: Deep learning for heterogeneous medical data analysis. World Wide Web 23(5), 2715\u20132737 (2020)","journal-title":"World Wide Web"},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Yue, L., Zhao, H., Yang, Y., Tian, D., Zhao, X., Yin, M.: A mimic learning method for disease risk prediction with incomplete initial data. In: International Conference on Database Systems for Advanced Applications, pp. 392\u2013396 (2019)","DOI":"10.1007\/978-3-030-18590-9_52"},{"key":"8_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, C., et al.: Towards better generalization for neural network-based sat solvers. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 199\u2013210 (2022)","DOI":"10.1007\/978-3-031-05936-0_16"},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, N., et al.: Document-level relation extraction as semantic segmentation. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, pp. 3999\u20134006 (2021)","DOI":"10.24963\/ijcai.2021\/551"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Peng, T., Han, R., Han, J., Yue, L., Liu, L.: Synchronously tracking entities and relations in a syntax-aware parallel architecture for aspect-opinion pair extraction. Appli. Intell. 1\u201316 (2022)","DOI":"10.1007\/s10489-022-03286-w"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Zhao, H., Huang, L., Zhang, R., Lu, Q., Xue, H.: SpanMlt: A span-based multi-task learning framework for pair-wise aspect and opinion terms extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3239\u20133248 (2020)","DOI":"10.18653\/v1\/2020.acl-main.296"},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Zhou, Y., et al.: Graph convolutional networks for target-oriented opinion words extraction with adversarial training. In: 2020 International Joint Conference on Neural Networks, pp. 1\u20137 (2020)","DOI":"10.1109\/IJCNN48605.2020.9207463"}],"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\/978-3-031-25198-6_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T07:57:02Z","timestamp":1676015822000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25198-6_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031251979","9783031251986"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25198-6_8","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":"10 February 2023","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":"Nanjing","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/apweb-waim2022.com\/proceedings","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"297","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"75","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5 Demo papers + 23 workshop papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}