{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:45:50Z","timestamp":1742996750783,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031619656"},{"type":"electronic","value":"9783031619663"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-61966-3_3","type":"book-chapter","created":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T01:03:59Z","timestamp":1717203839000},"page":"23-32","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Verbalizer for\u00a0Extracting Fine-Grained Customer Opinions from\u00a0Non-English Social Media Comments"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6244-936X","authenticated-orcid":false,"given":"Carlin C. F.","family":"Chu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6776-2563","authenticated-orcid":false,"given":"Jonathan H. Y.","family":"Leung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4336-7921","authenticated-orcid":false,"given":"Raymond","family":"So","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5683-217X","authenticated-orcid":false,"given":"Andy","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,1]]},"reference":[{"key":"3_CR1","unstructured":"Brown, T.B., et al.: Language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook (2020)"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Bu, J., et al.: ASAP: a Chinese review dataset towards aspect category sentiment analysis and rating prediction. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2069\u20132079. Association for Computational Linguistics (2021). https:\/\/www.aclweb.org\/anthology\/2021.naacl-main.167","DOI":"10.18653\/v1\/2021.naacl-main.167"},{"key":"3_CR3","doi-asserted-by":"publisher","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. International Committee on Computational Linguistics, Barcelona (2020). https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.72","DOI":"10.18653\/v1\/2020.coling-main.72"},{"key":"3_CR4","doi-asserted-by":"publisher","unstructured":"Cui, G., Hu, S., Ding, N., Huang, L., Liu, Z.: Prototypical verbalizer for prompt-based few-shot tuning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 7014\u20137024. Association for Computational Linguistics, Dublin (2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.483","DOI":"10.18653\/v1\/2022.acl-long.483"},{"key":"3_CR5","doi-asserted-by":"publisher","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, vol. 1 (Long and Short Papers). pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"3_CR6","doi-asserted-by":"publisher","unstructured":"Gao, T., Fisch, A., Chen, D.: Making pre-trained language models better few-shot learners. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, vol. 1: Long Papers, pp. 3816\u20133830. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.295, https:\/\/aclanthology.org\/2021.acl-long.295","DOI":"10.18653\/v1\/2021.acl-long.295"},{"key":"3_CR7","doi-asserted-by":"publisher","unstructured":"Hu, S., et al.: Knowledgeable prompt-tuning: incorporating knowledge into prompt verbalizer for text classification. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 2225\u20132240. Association for Computational Linguistics, Dublin (2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.158","DOI":"10.18653\/v1\/2022.acl-long.158"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Hu, S., et al.: Knowledgeable prompt-tuning: incorporating knowledge into prompt verbalizer for text classification. In: Proceedings of 60th Annual Meeting of the Association for Computational Linguistics. pp. 6311\u20136322. Association for Computational Linguistics, Dublin (2022). https:\/\/aclanthology.org\/2021.emnlp-main.509","DOI":"10.18653\/v1\/2022.acl-long.158"},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1007\/978-3-030-63031-7_28","volume-title":"Chinese Computational Linguistics","author":"Y Li","year":"2020","unstructured":"Li, Y., et al.: A joint model for aspect-category sentiment analysis with shared sentiment prediction layer. In: Sun, M., Li, S., Zhang, Y., Liu, Y., He, S., Rao, G. (eds.) CCL 2020, pp. 388\u2013400. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-63031-7_28"},{"key":"3_CR10","doi-asserted-by":"publisher","unstructured":"Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing (2021). https:\/\/doi.org\/10.48550\/ARXIV.2107.13586","DOI":"10.48550\/ARXIV.2107.13586"},{"key":"3_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560815","volume":"55","author":"P Liu","year":"2022","unstructured":"Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55, 1\u201335 (2022). https:\/\/doi.org\/10.1145\/3560815","journal-title":"ACM Comput. Surv."},{"key":"3_CR12","doi-asserted-by":"publisher","unstructured":"Ma, R., et al.: Template-free prompt tuning for few-shot NER. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 5721\u20135732. Association for Computational Linguistics, Seattle (2022). https:\/\/doi.org\/10.18653\/v1\/2022.naacl-main.420","DOI":"10.18653\/v1\/2022.naacl-main.420"},{"key":"3_CR13","unstructured":"SemEval-2016: International Workshop on Semantic Evaluation (2016). https:\/\/alt.qcri.org\/semeval2016\/task5"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Seoh, R., Birle, I., Tak, M., Chang, H.S., Pinette, B., Hough, A.: Open aspect target sentiment classification with natural language prompts. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 6311\u20136322. Association for Computational Linguistics, Online and Punta Cana (2021). https:\/\/aclanthology.org\/2021.emnlp-main.509","DOI":"10.18653\/v1\/2021.emnlp-main.509"},{"issue":"22","key":"3_CR15","doi-asserted-by":"publisher","first-page":"10542","DOI":"10.3390\/app112210542","volume":"11","author":"T Sharma","year":"2021","unstructured":"Sharma, T., Kaur, K.: Benchmarking deep learning methods for aspect level sentiment classification. Appl. Sci. 11(22), 10542 (2021). https:\/\/doi.org\/10.3390\/app112210542","journal-title":"Appl. Sci."},{"key":"3_CR16","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1007\/978-3-031-15931-2_19","volume-title":"Artificial Neural Networks and Machine Learning","author":"Y Wei","year":"2022","unstructured":"Wei, Y., Mo, T., Jiang, Y., Li, W., Zhao, W.: Eliciting knowledge from pretrained language models for prototypical prompt verbalizer. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds.) ICANN 2022, vol. 13530, pp. 222\u2013233. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-15931-2_19"},{"key":"3_CR17","doi-asserted-by":"publisher","unstructured":"You, Y., et al.: Ti-prompt: towards a prompt tuning method for few-shot threat intelligence twitter classification. In: 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 272\u2013279 (2022). https:\/\/doi.org\/10.1109\/COMPSAC54236.2022.00046","DOI":"10.1109\/COMPSAC54236.2022.00046"}],"container-title":["Communications in Computer and Information Science","HCI International 2024 Posters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-61966-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,22]],"date-time":"2024-12-22T04:16:06Z","timestamp":1734840966000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-61966-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031619656","9783031619663"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-61966-3_3","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Washington DC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}