{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:33:37Z","timestamp":1743057217058,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819724208"},{"type":"electronic","value":"9789819724215"}],"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\/978-981-97-2421-5_26","type":"book-chapter","created":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T08:01:48Z","timestamp":1715414508000},"page":"390-405","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fine-Grained Category Generation for\u00a0Sets of\u00a0Entities"],"prefix":"10.1007","author":[{"given":"Yexing","family":"Du","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jifan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianjun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,12]]},"reference":[{"issue":"3","key":"26_CR1","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1037\/h0076613","volume":"67","author":"EG Aiken","year":"1975","unstructured":"Aiken, E.G., Thomas, G.S., Shennum, W.A.: Memory for a lecture: effects of notes, lecture rate, and informational density. J. Educ. Psychol. 67(3), 439 (1975)","journal-title":"J. Educ. Psychol."},{"key":"26_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":"26_CR3","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Carlsson, F., \u00d6hman, J., Liu, F., Verlinden, S., Nivre, J., Sahlgren, M.: Fine-grained controllable text generation using non-residual prompting. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 6837\u20136857 (2022)","DOI":"10.18653\/v1\/2022.acl-long.471"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Choi, E., Levy, O., Choi, Y., Zettlemoyer, L.: Ultra-fine entity typing. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 87\u201396 (2018)","DOI":"10.18653\/v1\/P18-1009"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Ding, N., et al.: Prompt-learning for fine-grained entity typing. arXiv preprint arXiv:2108.10604 (2021)","DOI":"10.18653\/v1\/2022.findings-emnlp.512"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Han, X., et al.: Cross-lingual contrastive learning for fine-grained entity typing for low-resource languages. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2241\u20132250 (2022)","DOI":"10.18653\/v1\/2022.acl-long.159"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. Roy. Stat. Soc. Ser. C (Appl. Stati.) 28(1), 100\u2013108 (1979)","DOI":"10.2307\/2346830"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: COLING 1992 Volume 2: The 14th International Conference on Computational Linguistics (1992)","DOI":"10.3115\/992133.992154"},{"issue":"S1","key":"26_CR10","doi-asserted-by":"publisher","first-page":"S63","DOI":"10.1121\/1.2016299","volume":"62","author":"F Jelinek","year":"1977","unstructured":"Jelinek, F., Mercer, R.L., Bahl, L.R., Baker, J.K.: Perplexity-a measure of the difficulty of speech recognition tasks. J. Acoust. Soc. Am. 62(S1), S63\u2013S63 (1977)","journal-title":"J. Acoust. Soc. Am."},{"key":"26_CR11","unstructured":"Kenton, J.D.M.W.C., Toutanova, L.K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171\u20134186 (2019)"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3045\u20133059 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871\u20137880 (2020)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"26_CR14","unstructured":"Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74\u201381 (2004)"},{"issue":"9","key":"26_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560815","volume":"55","author":"P Liu","year":"2023","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(9), 1\u201335 (2023)","journal-title":"ACM Comput. Surv."},{"key":"26_CR16","unstructured":"Liu, X., et al.: GPT understands, too. arXiv preprint arXiv:2103.10385 (2021)"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Y., Shen, S., Lapata, M.: Noisy self-knowledge distillation for text summarization. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 692\u2013703 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.56"},{"key":"26_CR18","doi-asserted-by":"publisher","first-page":"39501","DOI":"10.1109\/ACCESS.2018.2855437","volume":"6","author":"E Min","year":"2018","unstructured":"Min, E., Guo, X., Liu, Q., Zhang, G., Cui, J., Long, J.: A survey of clustering with deep learning: from the perspective of network architecture. IEEE Access 6, 39501\u201339514 (2018)","journal-title":"IEEE Access"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Mota, T., Sridharan, M.: Commonsense reasoning and knowledge acquisition to guide deep learning on robots. In: Robotics: Science and Systems (2019)","DOI":"10.15607\/RSS.2019.XV.077"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Obeidat, R., Fern, X., Shahbazi, H., Tadepalli, P.: Description-based zero-shot fine-grained entity typing. 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. 807\u2013814 (2019)","DOI":"10.18653\/v1\/N19-1087"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Pang, K., Zhang, H., Zhou, J., Wang, T.: Divide and denoise: learning from noisy labels in fine-grained entity typing with cluster-wise loss correction. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1997\u20132006 (2022)","DOI":"10.18653\/v1\/2022.acl-long.141"},{"issue":"8","key":"26_CR22","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)","journal-title":"OpenAI Blog"},{"issue":"140","key":"26_CR23","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1\u201367 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"26_CR24","unstructured":"Reddy, D.R., et\u00a0al.: Speech understanding systems: a summary of results of the five-year research effort. Department of Computer Science. Camegie-Mell University, Pittsburgh, PA, vol. 17, p. 138 (1977)"},{"issue":"5","key":"26_CR25","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1016\/0306-4573(88)90021-0","volume":"24","author":"G Salton","year":"1988","unstructured":"Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513\u2013523 (1988)","journal-title":"Inf. Process. Manag."},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"See, A., Liu, P.J., Manning, C.D.: Get to the point: summarization with pointer-generator networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1073\u20131083 (2017)","DOI":"10.18653\/v1\/P17-1099"},{"key":"26_CR27","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1007\/978-3-319-71249-9_18","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"J Shen","year":"2017","unstructured":"Shen, J., Wu, Z., Lei, D., Shang, J., Ren, X., Han, J.: Setexpan: corpus-based set expansion via context feature selection and rank ensemble. In: Ceci, M., Hollm\u00e9n, J., Todorovski, L., Vens, C., Dzeroski, S. (eds.) Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 288\u2013304. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71249-9_18"},{"issue":"1","key":"26_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3419106","volume":"2","author":"T Shi","year":"2021","unstructured":"Shi, T., Keneshloo, Y., Ramakrishnan, N., Reddy, C.K.: Neural abstractive text summarization with sequence-to-sequence models. ACM Trans. Data Sci. 2(1), 1\u201337 (2021)","journal-title":"ACM Trans. Data Sci."},{"key":"26_CR29","doi-asserted-by":"crossref","unstructured":"Shin, T., Razeghi, Y., Logan\u00a0IV, R.L., Wallace, E., Singh, S.: Autoprompt: eliciting knowledge from language models with automatically generated prompts. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 4222\u20134235 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.346"},{"key":"26_CR30","unstructured":"Shleifer, S., Rush, A.M.: Pre-trained summarization distillation. arXiv preprint arXiv:2010.13002 (2020)"},{"key":"26_CR31","doi-asserted-by":"crossref","unstructured":"Wallace, E., Feng, S., Kandpal, N., Gardner, M., Singh, S.: Universal adversarial triggers for attacking and analyzing NLP. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 2153\u20132162 (2019)","DOI":"10.18653\/v1\/D19-1221"},{"key":"26_CR32","unstructured":"Zhang, J., Zhao, Y., Saleh, M., Liu, P.: Pegasus: pre-training with extracted gap-sentences for abstractive summarization. In: International Conference on Machine Learning, pp. 11328\u201311339. PMLR (2020)"},{"key":"26_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, S., Balog, K., Callan, J.: Generating categories for sets of entities. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1833\u20131842 (2020)","DOI":"10.1145\/3340531.3412019"},{"key":"26_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Shen, J., Shang, J., Han, J.: Empower entity set expansion via language model probing. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8151\u20138160 (2020)","DOI":"10.18653\/v1\/2020.acl-main.725"},{"key":"26_CR35","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khashabi, D., Tsai, C.T., Roth, D.: Zero-shot open entity typing as type-compatible grounding. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2065\u20132076 (2018)","DOI":"10.18653\/v1\/D18-1231"}],"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\/978-981-97-2421-5_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T08:07:34Z","timestamp":1715414854000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2421-5_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819724208","9789819724215"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2421-5_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"12 May 2024","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":"Wuhan","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.apweb-waim2023.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}