{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T06:48:44Z","timestamp":1746859724820,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031241963"},{"type":"electronic","value":"9783031241970"}],"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\/978-3-031-24197-0_4","type":"book-chapter","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T16:04:38Z","timestamp":1673971478000},"page":"59-73","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Semantic Label Representations with\u00a0Lbl2Vec: A Similarity-Based Approach for\u00a0Unsupervised Text Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3849-0394","authenticated-orcid":false,"given":"Tim","family":"Schopf","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8120-3368","authenticated-orcid":false,"given":"Daniel","family":"Braun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6667-5452","authenticated-orcid":false,"given":"Florian","family":"Matthes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","unstructured":"Braun, D., Klymenko, O., Schopf, T., Kaan Akan, Y., Matthes, F.: The language of engineering: training a domain-specific word embedding model for engineering. In: 2021 3rd International Conference on Management Science and Industrial Engineering, MSIE 2021, pp. 8\u201312. Association for Computing Machinery, New York (2021). https:\/\/doi.org\/10.1145\/3460824.3460826","DOI":"10.1145\/3460824.3460826"},{"key":"4_CR2","doi-asserted-by":"publisher","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LoF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD 2000, pp. 93\u2013104. Association for Computing Machinery, New York (2000). https:\/\/doi.org\/10.1145\/342009.335388","DOI":"10.1145\/342009.335388"},{"key":"4_CR3","unstructured":"Chang, M.W., Ratinov, L.A., Roth, D., Srikumar, V.: Importance of semantic representation: dataless classification. In: AAAI, pp. 830\u2013835 (2008). https:\/\/www.aaai.org\/Library\/AAAI\/2008\/aaai08-132.php"},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Chen, X., Xia, Y., Jin, P., Carroll, J.: Dataless text classification with descriptive LDA. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2015, pp. 2224\u20132231. AAAI Press (2015). https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI15\/paper\/view\/9524","DOI":"10.1609\/aaai.v29i1.9506"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41, 391\u2013407 (1990). https:\/\/cis.temple.edu\/vasilis\/Courses\/CIS750\/Papers\/deerwester90indexing_9.pdf","DOI":"10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9"},{"key":"4_CR6","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, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, pp. 4171\u20134186. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"4_CR7","unstructured":"Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI 2007, San Francisco, CA, USA, pp. 1606\u20131611. Morgan Kaufmann Publishers Inc. (2007). https:\/\/www.ijcai.org\/Proceedings\/07\/Papers\/259.pdf"},{"key":"4_CR8","doi-asserted-by":"publisher","unstructured":"Haj-Yahia, Z., Sieg, A., Deleris, L.A.: Towards unsupervised text classification leveraging experts and word embeddings. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, pp. 371\u2013379. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/P19-1036. https:\/\/aclanthology.org\/P19-1036","DOI":"10.18653\/v1\/P19-1036"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Lang, K.: Newsweeder: learning to filter netnews. In: Proceedings of the 12th International Machine Learning Conference (ML 1995) (1995)","DOI":"10.1016\/B978-1-55860-377-6.50048-7"},{"key":"4_CR10","unstructured":"Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Xing, E.P., Jebara, T. (eds.) Proceedings of the 31st International Conference on Machine Learning. Proceedings of Machine Learning Research, Bejing, China, vol. 32, pp. 1188\u20131196. PMLR (2014). https:\/\/proceedings.mlr.press\/v32\/le14.html"},{"key":"4_CR11","unstructured":"Li, Y., Zheng, R., Tian, T., Hu, Z., Iyer, R., Sycara, K.: Joint embedding of hierarchical categories and entities for concept categorization and dataless classification. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan, pp. 2678\u20132688. The COLING 2016 Organizing Committee (2016). https:\/\/aclanthology.org\/C16-1252"},{"key":"4_CR12","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 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.724","DOI":"10.18653\/v1\/2020.emnlp-main.724"},{"key":"4_CR13","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 26. Curran Associates, Inc. (2013). https:\/\/proceedings.neurips.cc\/paper\/2013\/file\/9aa42b31882ec039965f3c4923ce901b-Paper.pdf"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Nam, J., Menc\u00eda, E.L., F\u00fcrnkranz, J.: All-in text: learning document, label, and word representations jointly. In: AAAI Conference on Artificial Intelligence (2016). https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI16\/paper\/view\/12058","DOI":"10.1609\/aaai.v30i1.10241"},{"key":"4_CR15","doi-asserted-by":"publisher","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), Hong Kong, China, pp. 3982\u20133992. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1410. https:\/\/aclanthology.org\/D19-1410","DOI":"10.18653\/v1\/D19-1410"},{"key":"4_CR16","unstructured":"Sappadla, P.V., Nam, J., Mencia, E.L., F\u00fcrnkranz, J.: Using semantic similarity for multi-label zero-shot classification of text documents. In: Proceedings of European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2016). https:\/\/www.esann.org\/sites\/default\/files\/proceedings\/legacy\/es2016-174.pdf"},{"key":"4_CR17","unstructured":"Schneider, P., Schopf, T., Vladika, J., Galkin, M., Simperl, E., Matthes, F.: A decade of knowledge graphs in natural language processing: a survey. In: Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, pp. 601\u2013614. Association for Computational Linguistics (2022). https:\/\/aclanthology.org\/2022.aacl-main.46"},{"key":"4_CR18","doi-asserted-by":"publisher","unstructured":"Schopf, T., Braun, D., Matthes, F.: Lbl2Vec: an embedding-based approach for unsupervised document retrieval on predefined topics. In: Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST, pp. 124\u2013132. INSTICC, SciTePress (2021). https:\/\/doi.org\/10.5220\/0010710300003058","DOI":"10.5220\/0010710300003058"},{"key":"4_CR19","unstructured":"Schopf, T., Braun, D., Matthes, F.: Lbl2Vec github repository (2021). https:\/\/github.com\/sebischair\/Lbl2Vec"},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"Schopf, T., Braun, D., Matthes, F.: Evaluating unsupervised text classification: zero-shot and similarity-based approaches. In: 2022 6th International Conference on Natural Language Processing and Information Retrieval (NLPIR), NLPIR 2022. Association for Computing Machinery, New York (2023)","DOI":"10.1145\/3582768.3582795"},{"key":"4_CR21","doi-asserted-by":"publisher","unstructured":"Schopf, T., Klimek, S., Matthes, F.: Patternrank: leveraging pretrained language models and part of speech for unsupervised keyphrase extraction. In: Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR, pp. 243\u2013248. INSTICC, SciTePress (2022). https:\/\/doi.org\/10.5220\/0011546600003335","DOI":"10.5220\/0011546600003335"},{"key":"4_CR22","doi-asserted-by":"publisher","unstructured":"Schopf, T., Weinberger, P., Kinkeldei, T., Matthes, F.: Towards bilingual word embedding models for engineering. In: 2022 4th International Conference on Management Science and Industrial Engineering, MSIE 2022. Association for Computing Machinery, New York (2022). https:\/\/doi.org\/10.1145\/3535782.3535835","DOI":"10.1145\/3535782.3535835"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Song, Y., Roth, D.: On dataless hierarchical text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28, no. 1 (2014). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/8938","DOI":"10.1609\/aaai.v28i1.8938"},{"key":"4_CR24","unstructured":"Song, Y., Upadhyay, S., Peng, H., Roth, D.: Cross-lingual dataless classification for many languages. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, pp. 2901\u20132907. AAAI Press (2016). https:\/\/www.ijcai.org\/Proceedings\/16\/Papers\/412.pdf"},{"key":"4_CR25","unstructured":"Stammbach, D., Ash, E.: DocSCAN: unsupervised text classification via learning from neighbors. arXiv abs\/2105.04024 (2021). https:\/\/arxiv.org\/abs\/2105.04024"},{"key":"4_CR26","doi-asserted-by":"publisher","unstructured":"Wang, W., Zheng, V.W., Yu, H., Miao, C.: A survey of zero-shot learning: settings, methods, and applications. ACM Trans. Intell. Syst. Technol. 10(2) (2019). https:\/\/doi.org\/10.1145\/3293318","DOI":"10.1145\/3293318"},{"key":"4_CR27","doi-asserted-by":"publisher","unstructured":"Williams, A., Nangia, N., Bowman, S.: A broad-coverage challenge corpus for sentence understanding through inference. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), New Orleans, Louisiana, pp. 1112\u20131122. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/N18-1101. https:\/\/aclanthology.org\/N18-1101","DOI":"10.18653\/v1\/N18-1101"},{"key":"4_CR28","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: XLNet: Generalized Autoregressive Pretraining for Language Understanding. Curran Associates Inc., Red Hook (2019)"},{"key":"4_CR29","doi-asserted-by":"publisher","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. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.272. https:\/\/aclanthology.org\/2020.acl-main.272","DOI":"10.18653\/v1\/2020.acl-main.272"},{"key":"4_CR30","doi-asserted-by":"publisher","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), Hong Kong, China, pp. 3914\u20133923. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1404. https:\/\/aclanthology.org\/D19-1404","DOI":"10.18653\/v1\/D19-1404"},{"key":"4_CR31","doi-asserted-by":"publisher","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, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, pp. 1031\u20131040. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1108. https:\/\/aclanthology.org\/N19-1108","DOI":"10.18653\/v1\/N19-1108"},{"key":"4_CR32","unstructured":"Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28. Curran Associates, Inc. (2015). https:\/\/proceedings.neurips.cc\/paper\/2015\/file\/250cf8b51c773f3f8dc8b4be867a9a02-Paper.pdf"},{"key":"4_CR33","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Meng, Y., Huang, J., Xu, F.F., Wang, X., Han, J.: Minimally Supervised Categorization of Text with Metadata, pp. 1231\u20131240. Association for Computing Machinery, New York (2020). https:\/\/doi.org\/10.1145\/3397271.3401168","DOI":"10.1145\/3397271.3401168"}],"container-title":["Lecture Notes in Business Information Processing","Web Information Systems and Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-24197-0_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T21:48:34Z","timestamp":1701726514000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-24197-0_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031241963","9783031241970"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-24197-0_4","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"type":"print","value":"1865-1348"},{"type":"electronic","value":"1865-1356"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"18 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WEBIST","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems and Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"webist2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/webist.scitevents.org\/?y=2021","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":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"107","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":"22","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":"35","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":"21% - 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)"}}]}}