{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:16:01Z","timestamp":1743027361958,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031084720"},{"type":"electronic","value":"9783031084737"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-08473-7_11","type":"book-chapter","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T18:03:10Z","timestamp":1655402590000},"page":"119-126","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised Ranking and\u00a0Aggregation of\u00a0Label Descriptions for\u00a0Zero-Shot Classifiers"],"prefix":"10.1007","author":[{"given":"Angelo","family":"Basile","sequence":"first","affiliation":[]},{"given":"Marc","family":"Franco-Salvador","sequence":"additional","affiliation":[]},{"given":"Paolo","family":"Rosso","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,13]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Artstein, R., Poesio, M.: Survey article: inter-coder agreement for computational linguistics. Comput. Linguist. 555\u2013596 (2008)","DOI":"10.1162\/coli.07-034-R2"},{"key":"11_CR2","unstructured":"Brown, T., Mann, B., Ryder, N., et al.: Language models are few-shot learners. Adv. NIPS 1877\u20131901 (2020)"},{"key":"11_CR3","unstructured":"Chang, M.W., Ratinov, L.A., Roth, D., et al.: Importance of semantic representation: dataless classification. In: AAAI, pp. 830\u2013835 (2008)"},{"key":"11_CR4","unstructured":"Devlin, J., Chang, M.W., Lee, K., et al.: 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, Volume 1 (Long and Short Papers), pp. 4171\u20134186 (2019)"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Fornaciari, T., Uma, A., Paun, S., et al.: Beyond black & white: leveraging annotator disagreement via soft-label multi-task learning. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2591\u20132597 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.204"},{"key":"11_CR6","unstructured":"Gulli, A.: AG\u2019s corpus of news articles (2005). http:\/\/groups.di.unipi.it\/~gulli\/AG_corpus_of_news_articles.html"},{"key":"11_CR7","unstructured":"Hovy, D., Berg-Kirkpatrick, T., Vaswani, A., et al.: Learning whom to trust with MACE. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1120\u20131130 (2013)"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Xu, F.F., Araki, J., et al.: How can we know what language models know? Trans. Assoc. Comput. Linguist. 423\u2013438 (2020)","DOI":"10.1162\/tacl_a_00324"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Junczys-Dowmunt, M., Grundkiewicz, R., Dwojak, T., et al.: Marian: fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations, pp. 116\u2013121 (2018)","DOI":"10.18653\/v1\/P18-4020"},{"key":"11_CR10","unstructured":"Liu, P., Yuan, W., Fu, J., et al.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586 (2021)"},{"key":"11_CR11","unstructured":"Maas, A.L., Daly, R.E., Pham, P.T., et al.: Learning word vectors for sentiment analysis. In: ACL, pp. 142\u2013150 (2011)"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"M\u00fcller, T., P\u00e9rez-Torr\u00f3, G., Franco-Salvador, M.: Few-shot learning with siamese networks and label tuning. In: ACL (2022)","DOI":"10.18653\/v1\/2022.acl-long.584"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Passonneau, R.J., Carpenter, B.: The benefits of a model of annotation. Trans. Assoc. Comput. Linguist. 311\u2013326 (2014)","DOI":"10.1162\/tacl_a_00185"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Paun, S., Carpenter, B., Chamberlain, J., et al.: Comparing Bayesian models of annotation. Trans. Assoc. Comput. Linguist. 571\u2013585 (2018)","DOI":"10.1162\/tacl_a_00040"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Plank, B., Hovy, D., S\u00f8gaard, A.: Linguistically debatable or just plain wrong? In: ACL, pp. 507\u2013511 (2014)","DOI":"10.3115\/v1\/P14-2083"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: EMNLP, pp. 3982\u20133992 (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: The curse of dense low-dimensional information retrieval for large index sizes. In: ACL, pp. 605\u2013611 (2021)","DOI":"10.18653\/v1\/2021.acl-short.77"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Schick, T., Sch\u00fctze, H.: Exploiting cloze-questions for few-shot text classification and natural language inference. In: EACL, pp. 255\u2013269 (2021)","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Simpson, E., Roberts, S., Psorakis, I., et al.: Dynamic Bayesian combination of multiple imperfect classifiers. In: Decision Making and Imperfection, pp. 1\u201335 (2013)","DOI":"10.1007\/978-3-642-36406-8_1"},{"key":"11_CR20","unstructured":"Taul\u00e9, M., Mart\u00ed, M.A., Rangel, F.M., et al.: Overview of the task on stance and gender detection in tweets on catalan independence at ibereval 2017. In: 2nd Workshop on Evaluation of Human Language Technologies for Iberian Languages, IberEval 2017 (CEUR-WS ), pp. 157\u2013177 (2017)"},{"key":"11_CR21","unstructured":"Tiedemann, J., Thottingal, S.: OPUS-MT - Building open translation services for the World. In: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation (EAMT), Lisbon, Portugal (2020)"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Uma, A., Fornaciari, T., Dumitrache, A., et al.: SemEval-2021 task 12: learning with disagreements. In: Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pp. 338\u2013347 (2021)","DOI":"10.18653\/v1\/2021.semeval-1.41"},{"key":"11_CR23","unstructured":"Wang, S., Fang, H., Khabsa, M., et al.: Entailment as few-shot learner. arXiv preprint arXiv:2104.14690 (2021)"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Warstadt, A., Singh, A., Bowman, S.R.: Neural network acceptability judgments. arXiv preprint arXiv:1805.12471 (2018)","DOI":"10.1162\/tacl_a_00290"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., et al.: Transformers: state-of-the-art natural language processing. In: EMNLP, pp. 38\u201345 (2020)","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Yin, W., Hay, J., Roth, D.: Benchmarking zero-shot text classification: datasets, evaluation and entailment approach. In: EMNLP, pp. 3914\u20133923 (2019)","DOI":"10.18653\/v1\/D19-1404"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Yin, W., Rajani, N.F., Radev, D., et al.: Universal natural language processing with limited annotations: try few-shot textual entailment as a start. In: EMNLP, pp. 8229\u20138239 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.660"},{"key":"11_CR28","unstructured":"Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. Adv. NIPS (2015)"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-08473-7_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T18:04:52Z","timestamp":1655402692000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08473-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031084720","9783031084737"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08473-7_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"13 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLDB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applications of Natural Language to Information Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Valencia","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"15 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nldb2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/nldb2022.prhlt.upv.es\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"106","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":"28","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":"20","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":"26% - 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":"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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}