{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T15:26:56Z","timestamp":1744990016981,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"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_4","type":"book-chapter","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T18:03:10Z","timestamp":1655402590000},"page":"35-46","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Using Pseudo-Labelled Data for\u00a0Zero-Shot Text Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6800-7642","authenticated-orcid":false,"given":"Congcong","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7214-4666","authenticated-orcid":false,"given":"Paul","family":"Nulty","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5702-4463","authenticated-orcid":false,"given":"David","family":"Lillis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,13]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Alam, F., Qazi, U., Imran, M., Ofli, F.: Humaid: human-annotated disaster incidents data from twitter with deep learning benchmarks. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 933\u2013942 (2021)","DOI":"10.1609\/icwsm.v15i1.18116"},{"key":"4_CR2","unstructured":"Davison, J.: Zero-shot classifier distillation (2021). https:\/\/github.com\/huggingface\/transformers\/tree\/master\/examples\/research_projects\/zero-shot-distillation"},{"key":"4_CR3","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 (June 2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Karamanolakis, G., Mukherjee, S., Zheng, G., Hassan, A.: Self-training with weak supervision. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 845\u2013863 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.66"},{"key":"4_CR5","doi-asserted-by":"publisher","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP, pp. 1746\u20131751. Association for Computational Linguistics, Doha (October 2014). https:\/\/doi.org\/10.3115\/v1\/D14-1181","DOI":"10.3115\/v1\/D14-1181"},{"key":"4_CR6","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference for Learning Representations, San Diego (2015)"},{"key":"4_CR7","unstructured":"Klinger, R., et al.: An analysis of annotated corpora for emotion classification in text. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 2104\u20132119 (2018)"},{"key":"4_CR8","unstructured":"Lee, D.H., et al.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on challenges in representation learning, ICML (2013)"},{"key":"4_CR9","doi-asserted-by":"publisher","unstructured":"Levy, O., Seo, M., Choi, E., Zettlemoyer, L.: Zero-shot relation extraction via reading comprehension. In: Proceedings of the 21st Conference on Computational Natural Language Learning, CoNLL 2017, pp. 333\u2013342. Association for Computational Linguistics, Vancouver (August 2017). https:\/\/doi.org\/10.18653\/v1\/K17-1034","DOI":"10.18653\/v1\/K17-1034"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Mekala, D., Shang, J.: Contextualized weak supervision for text classification. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 323\u2013333 (2020)","DOI":"10.18653\/v1\/2020.acl-main.30"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Mekala, D., Zhang, X., Shang, J.: Meta: metadata-empowered weak supervision for text classification. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP, pp. 8351\u20138361 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.670"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Meng, Y., Shen, J., Zhang, C., Han, J.: Weakly-supervised neural text classification. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, vol. 2018 (2018)","DOI":"10.1145\/3269206.3271737"},{"key":"4_CR13","doi-asserted-by":"publisher","unstructured":"Meng, Y., et al.: Text classification using label names only: a language model self-training approach. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP, pp. 9006\u20139017. Association for Computational Linguistics (November 2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.724","DOI":"10.18653\/v1\/2020.emnlp-main.724"},{"key":"4_CR14","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 (2022). arXiv preprint, arXiv:2203.14655","DOI":"10.18653\/v1\/2022.acl-long.584"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Obamuyide, A., Vlachos, A.: Zero-shot relation classification as textual entailment. In: Proceedings of the First Workshop on Fact Extraction and VERification, FEVER, pp. 72\u201378 (2018)","DOI":"10.18653\/v1\/W18-5511"},{"key":"4_CR16","unstructured":"Puri, R., Catanzaro, B.: Zero-shot text classification with generative language models (2019). CoRR, abs\/1912.10165"},{"key":"4_CR17","unstructured":"Pushp, P.K., Srivastava, M.M.: Train once, test anywhere: zero-shot learning for text classification (2017). CoRR, abs\/1712.05972"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-bert: sentence embeddings using siamese bert-networks. 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. 3973\u20133983 (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Saravia, E., Liu, H.C.T., Huang, Y.H., Wu, J., Chen, Y.S.: Carer: contextualized affect representations for emotion recognition. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3687\u20133697 (2018)","DOI":"10.18653\/v1\/D18-1404"},{"key":"4_CR20","unstructured":"Veeranna, S.P., Nam, J., Menc\u0131a, E.L., F\u00fcrnkranz, J.: Using semantic similarity for multi-label zero-shot classification of text documents. In: Proceeding of European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 423\u2013428. Elsevier, Bruges (2016)"},{"key":"4_CR21","doi-asserted-by":"publisher","unstructured":"Wang, C., Lillis, D.: A comparative study on word embeddings in deep learning for text classification. In: Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval, NLPIR 2020, Seoul, South Korea (December 2020). https:\/\/doi.org\/10.1145\/3443279.3443304","DOI":"10.1145\/3443279.3443304"},{"key":"4_CR22","first-page":"705","volume-title":"ISCRAM 2021 Conference Proceedings \u2013 18th International Conference on Information Systems for Crisis Response and Management","author":"C Wang","year":"2021","unstructured":"Wang, C., Nulty, P., Lillis, D.: Transformer-based multi-task learning for disaster tweet categorisation. In: Adrot, A., Grace, R., Moore, K., Zobel, C.W. (eds.) ISCRAM 2021 Conference Proceedings \u2013 18th International Conference on Information Systems for Crisis Response and Management, pp. 705\u2013718. Virginia Tech., Blacksburg (2021)"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Wang, Z., Mekala, D., Shang, J.: X-class: text classification with extremely weak supervision. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 3043\u20133053 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.242"},{"key":"4_CR24","doi-asserted-by":"publisher","unstructured":"Wolf, T., et al.: Transformers:state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38\u201345. Association for Computational Linguistics (October 2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlpdemos.6","DOI":"10.18653\/v1\/2020.emnlpdemos.6"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Xia, C., Zhang, C., Yan, X., Chang, Y., Philip, S.Y.: Zero-shot user intent detection via capsule neural networks. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3090\u20133099 (2018)","DOI":"10.18653\/v1\/D18-1348"},{"key":"4_CR26","unstructured":"Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: International Conference on Machine Learning, PMLR, pp. 478\u2013487 (2016)"},{"key":"4_CR27","doi-asserted-by":"crossref","unstructured":"Ye, Z., et al.: Zero-shot text classification via reinforced self-training. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3014\u20133024 (2020)","DOI":"10.18653\/v1\/2020.acl-main.272"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Yin, W., Hay, J., Roth, D.: Benchmarking zero-shot text classification: datasets, evaluation and entailment approach. 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. 3905\u20133914 (2019)","DOI":"10.18653\/v1\/D19-1404"},{"key":"4_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, J., Lertvittayakumjorn, P., Guo, Y.: Integrating semantic knowledge to tackle zero-shot text classification. 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. 1031\u20131040 (2019)","DOI":"10.18653\/v1\/N19-1108"},{"key":"4_CR30","unstructured":"Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, vol. 1, pp. 649\u2013657 (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_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T16:44:18Z","timestamp":1675874658000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08473-7_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031084720","9783031084737"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08473-7_4","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)"}}]}}