{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:18:02Z","timestamp":1743027482021,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031301100"},{"type":"electronic","value":"9783031301117"}],"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\/978-3-031-30111-7_43","type":"book-chapter","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T05:02:51Z","timestamp":1681275771000},"page":"509-520","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prompt-Based Learning for\u00a0Aspect-Level Sentiment Classification"],"prefix":"10.1007","author":[{"given":"Guowei","family":"Li","sequence":"first","affiliation":[]},{"given":"Fuqiang","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Wangqun","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Diwen","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,13]]},"reference":[{"key":"43_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106423","volume":"213","author":"OM Beigi","year":"2021","unstructured":"Beigi, O.M., Moattar, M.H.: Automatic construction of domain-specific sentiment lexicon for unsupervised domain adaptation and sentiment classification. Knowl. Based Syst. 213, 106423 (2021)","journal-title":"Knowl. Based Syst."},{"key":"43_CR2","unstructured":"Ben-David, E., Oved, N., Reichart, R.: PADA: a prompt-based autoregressive approach for adaptation to unseen domains. arXiv preprint arXiv:2102.12206 (2021)"},{"key":"43_CR3","unstructured":"Brown, T.B., et al.: Language models are few-shot learners. In: Proceedings of NeurIPS, pp. 1877\u20131901 (2020)"},{"issue":"4","key":"43_CR4","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1080\/09540091.2021.1912711","volume":"33","author":"Z Cao","year":"2021","unstructured":"Cao, Z., Zhou, Y., Yang, A., Peng, S.: Deep transfer learning mechanism for fine-grained cross-domain sentiment classification. Connect. Sci. 33(4), 911\u2013928 (2021)","journal-title":"Connect. Sci."},{"key":"43_CR5","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL, pp. 4171\u20134186 (2019)"},{"key":"43_CR6","doi-asserted-by":"crossref","unstructured":"Gao, T., Fisch, A., Chen, D.: Making pre-trained language models better few-shot learners. In: Proceedings of ACL, pp. 3816\u20133830 (2021)","DOI":"10.18653\/v1\/2021.acl-long.295"},{"key":"43_CR7","doi-asserted-by":"crossref","unstructured":"Hambardzumyan, K., Khachatrian, H., May, J.: WARP: word-level adversarial reprogramming. In: Proceedings of ACL, pp. 4921\u20134933 (2021)","DOI":"10.18653\/v1\/2021.acl-long.381"},{"key":"43_CR8","doi-asserted-by":"crossref","unstructured":"Heinzerling, B., Inui, K.: Language models as knowledge bases: on entity representations, storage capacity, and paraphrased queries. In: Proceedings of EACL, pp. 1772\u20131791 (2021)","DOI":"10.18653\/v1\/2021.eacl-main.153"},{"key":"43_CR9","doi-asserted-by":"crossref","unstructured":"Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. In: Gurevych, I., Miyao, Y. (eds.) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15\u201320 July 2018, Volume 1: Long Papers, pp. 328\u2013339. Association for Computational Linguistics (2018)","DOI":"10.18653\/v1\/P18-1031"},{"key":"43_CR10","doi-asserted-by":"crossref","unstructured":"Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of ACM SIGKDD, pp. 168\u2013177 (2004)","DOI":"10.1145\/1014052.1014073"},{"key":"43_CR11","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1162\/tacl_a_00324","volume":"8","author":"Z Jiang","year":"2020","unstructured":"Jiang, Z., Xu, F.F., Araki, J., Neubig, G.: How can we know what language models know. Trans. Assoc. Comput. Linguist. 8, 423\u2013438 (2020)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"43_CR12","doi-asserted-by":"crossref","unstructured":"Li, L., Liu, Y., Zhou, A.: Hierarchical attention based position-aware network for aspect-level sentiment analysis. In: Proceedings of CoNLL, pp. 181\u2013189 (2018)","DOI":"10.18653\/v1\/K18-1018"},{"key":"43_CR13","unstructured":"Li, X.L., Liang, P.: Prefix-tuning: optimizing continuous prompts for generation. In: Proceedings of ACL, pp. 4582\u20134597 (2021)"},{"key":"43_CR14","doi-asserted-by":"crossref","unstructured":"Li, Z., Qin, Y., Liu, Z., Wang, W.: Powering comparative classification with sentiment analysis via domain adaptive knowledge transfer. arXiv preprint arXiv:2109.03819 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.546"},{"key":"43_CR15","unstructured":"Liu, X., et al.: GPT understands, too. arXiv preprint arXiv:2103.10385 (2021)"},{"key":"43_CR16","doi-asserted-by":"crossref","unstructured":"Logan, R.L., IV., Bala\u017eevi\u0107, I., Wallace, E., Petroni, F., Singh, S., Riedel, S.: Cutting down on prompts and parameters: simple few-shot learning with language models. arXiv preprint arXiv:2106.13353 (2021)","DOI":"10.18653\/v1\/2022.findings-acl.222"},{"key":"43_CR17","doi-asserted-by":"crossref","unstructured":"Ni, J., Li, J., McAuley, J.J.: Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: Proceedings of EMNLP, pp. 188\u2013197 (2019)","DOI":"10.18653\/v1\/D19-1018"},{"key":"43_CR18","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 COLING, pp. 27\u201335 (2014)","DOI":"10.3115\/v1\/S14-2004"},{"key":"43_CR19","unstructured":"Rietzler, A., Stabinger, S., Opitz, P., Engl, S.: Adapt or get left behind: domain adaptation through BERT language model finetuning for aspect-target sentiment classification. In: Proceedings of LREC, pp. 4933\u20134941 (2020)"},{"key":"43_CR20","doi-asserted-by":"crossref","unstructured":"Schick, T., Sch\u00fctze, H.: Exploiting cloze-questions for few-shot text classification and natural language inference. In: Proceedings of EACL, pp. 255\u2013269 (2021)","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"key":"43_CR21","doi-asserted-by":"crossref","unstructured":"Schick, T., Sch\u00fctze, H.: It\u2019s not just size that matters: small language models are also few-shot learners. In: Proceedings of NAACL, pp. 2339\u20132352 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.185"},{"key":"43_CR22","doi-asserted-by":"crossref","unstructured":"Seoh, R., Birle, I., Tak, M., Chang, H., Pinette, B., Hough, A.: Open aspect target sentiment classification with natural language prompts. In: Proceedings of EMNLP, pp. 6311\u20136322 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.509"},{"key":"43_CR23","doi-asserted-by":"crossref","unstructured":"Shin, T., Razeghi, Y., IV., R.L.L., Wallace, E., Singh, S.: AutoPrompt: eliciting knowledge from language models with automatically generated prompts. In: Proceedings of EMNLP, pp. 4222\u20134235 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.346"},{"key":"43_CR24","unstructured":"Sun, C., Huang, L., Qiu, X.: Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. In: Proceedings of NAACL, pp. 380\u2013385 (2019)"},{"key":"43_CR25","doi-asserted-by":"crossref","unstructured":"Tang, H., Ji, D., Li, C., Zhou, Q.: Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In: Proceedings of ACL, pp. 6578\u20136588 (2020)","DOI":"10.18653\/v1\/2020.acl-main.588"},{"key":"43_CR26","unstructured":"Wolf, T., et al.: HuggingFace\u2019s transformers: state-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019)"},{"key":"43_CR27","unstructured":"Xu, H., Liu, B., Shu, L., Yu, P.S.: BERT post-training for review reading comprehension and aspect-based sentiment analysis. In: Proceedings of NAACL, pp. 2324\u20132335 (2019)"},{"key":"43_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 ACL, pp. 2416\u20132429 (2021)","DOI":"10.18653\/v1\/2021.acl-long.188"},{"key":"43_CR29","doi-asserted-by":"crossref","unstructured":"Yin, W., Hay, J., Roth, D.: Benchmarking zero-shot text classification: datasets, evaluation and entailment approach. In: Proceedings of EMNLP, pp. 3912\u20133921 (2019)","DOI":"10.18653\/v1\/D19-1404"},{"key":"43_CR30","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.ins.2021.07.001","volume":"578","author":"C Zhao","year":"2021","unstructured":"Zhao, C., Wang, S., Li, D., Liu, X., Yang, X., Liu, J.: Cross-domain sentiment classification via parameter transferring and attention sharing mechanism. Inf. Sci. 578, 281\u2013296 (2021)","journal-title":"Inf. Sci."}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30111-7_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T05:13:14Z","timestamp":1681276394000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30111-7_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031301100","9783031301117"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30111-7_43","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":"13 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"22 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2022.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"810","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":"359","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":"0","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":"44% - 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":"2.65","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":"3","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":"ICONIP 2022 consists of a two-volume set, LNCS & CCIS, which includes 146 and 213 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)"}}]}}