{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:48:11Z","timestamp":1743083291934,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819722617"},{"type":"electronic","value":"9789819722594"}],"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\/978-981-97-2259-4_22","type":"book-chapter","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T09:02:31Z","timestamp":1713949351000},"page":"290-301","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Contrastive Learning for\u00a0Unsupervised Sentence Embedding with\u00a0False Negative Calibration"],"prefix":"10.1007","author":[{"given":"Chi-Min","family":"Chiu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying-Jia","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hung-Yu","family":"Kao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,25]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","unstructured":"Agirre, E., et al.: SemEval-2015 task 2: semantic textual similarity, English, Spanish and pilot on interpretability. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 252\u2013263. Association for Computational Linguistics, Denver, Colorado (2015). https:\/\/doi.org\/10.18653\/v1\/S15-2045, https:\/\/aclanthology.org\/S15-2045","DOI":"10.18653\/v1\/S15-2045"},{"key":"22_CR2","doi-asserted-by":"publisher","unstructured":"Agirre, E., et al.: SemEval-2014 task 10: multilingual semantic textual similarity. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 81\u201391. Association for Computational Linguistics, Dublin, Ireland (2014). https:\/\/doi.org\/10.3115\/v1\/S14-2010, https:\/\/aclanthology.org\/S14-2010","DOI":"10.3115\/v1\/S14-2010"},{"key":"22_CR3","doi-asserted-by":"publisher","unstructured":"Agirre, E., et al.: SemEval-2016 task 1: semantic textual similarity, monolingual and cross-lingual evaluation. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 497\u2013511. Association for Computational Linguistics, San Diego, California (2016). https:\/\/doi.org\/10.18653\/v1\/S16-1081, https:\/\/aclanthology.org\/S16-1081","DOI":"10.18653\/v1\/S16-1081"},{"key":"22_CR4","unstructured":"Agirre, E., Cer, D., Diab, M., Gonzalez-Agirre, A.: SemEval-2012 task 6: a pilot on semantic textual similarity. In: *SEM 2012: The First Joint Conference on Lexical and Computational Semantics \u2013 Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012), pp. 385\u2013393. Association for Computational Linguistics, Montr\u00e9al, Canada (2012). https:\/\/aclanthology.org\/S12-1051"},{"key":"22_CR5","unstructured":"Agirre, E., Cer, D., Diab, M., Gonzalez-Agirre, A., Guo, W.: *SEM 2013 shared task: semantic textual similarity. In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity, pp. 32\u201343. Association for Computational Linguistics, Atlanta, Georgia, USA (2013). https:\/\/aclanthology.org\/S13-1004"},{"key":"22_CR6","doi-asserted-by":"publisher","unstructured":"Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., Specia, L.: SemEval-2017 task 1: semantic textual similarity multilingual and crosslingual focused evaluation. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 1\u201314. Association for Computational Linguistics, Vancouver, Canada (2017). https:\/\/doi.org\/10.18653\/v1\/S17-2001, https:\/\/aclanthology.org\/S17-2001","DOI":"10.18653\/v1\/S17-2001"},{"key":"22_CR7","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a0119, pp. 1597\u20131607. PMLR (2020)"},{"key":"22_CR8","unstructured":"Chen, T.S., Hung, W.C., Tseng, H.Y., Chien, S.Y., Yang, M.H.: Incremental false negative detection for contrastive learning. arXiv preprint arXiv:2106.03719 (2021)"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Chuang, Y.S., et al.: DiffCSE: difference-based contrastive learning for sentence embeddings. In: Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) (2022)","DOI":"10.18653\/v1\/2022.naacl-main.311"},{"key":"22_CR10","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), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423, https:\/\/aclanthology.org\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Gao, T., Yao, X., Chen, D.: SimCSE: simple contrastive learning of sentence embeddings. In: Empirical Methods in Natural Language Processing (EMNLP) (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"22_CR12","doi-asserted-by":"publisher","unstructured":"Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol.\u00a02, pp. 1735\u20131742 (2006). https:\/\/doi.org\/10.1109\/CVPR.2006.100","DOI":"10.1109\/CVPR.2006.100"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Jiang, T., et al.: PromptBERT: improving BERT sentence embeddings with prompts. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 8826\u20138837. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (2022). https:\/\/aclanthology.org\/2022.emnlp-main.603","DOI":"10.18653\/v1\/2022.emnlp-main.603"},{"key":"22_CR14","unstructured":"Kiros, R., et al.: Skip-thought vectors. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a028. Curran Associates, Inc. (2015). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2015\/file\/f442d33fa06832082290ad8544a8da27-Paper.pdf"},{"key":"22_CR15","doi-asserted-by":"publisher","unstructured":"Li, B., Zhou, H., He, J., Wang, M., Yang, Y., Li, L.: On the sentence embeddings from pre-trained language models. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9119\u20139130. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.733, https:\/\/aclanthology.org\/2020.emnlp-main.733","DOI":"10.18653\/v1\/2020.emnlp-main.733"},{"key":"22_CR16","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"22_CR17","unstructured":"Logeswaran, L., Lee, H.: An efficient framework for learning sentence representations. In: International Conference on Learning Representations (2018). https:\/\/openreview.net\/forum?id=rJvJXZb0W"},{"key":"22_CR18","unstructured":"Marelli, M., Menini, S., Baroni, M., Bentivogli, L., Bernardi, R., Zamparelli, R.: A SICK cure for the evaluation of compositional distributional semantic models. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014), pp. 216\u2013223. European Language Resources Association (ELRA), Reykjavik, Iceland (2014). http:\/\/www.lrec-conf.org\/proceedings\/lrec2014\/pdf\/$363_Paper$.pdf"},{"key":"22_CR19","doi-asserted-by":"publisher","unstructured":"Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543. Association for Computational Linguistics, Doha, Qatar (2014). https:\/\/doi.org\/10.3115\/v1\/D14-1162, https:\/\/aclanthology.org\/D14-1162","DOI":"10.3115\/v1\/D14-1162"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Phan, X.H., Nguyen, L.M., Horiguchi, S.: Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In: Proceedings of the 17th international conference on World Wide Web, pp. 91\u2013100 (2008)","DOI":"10.1145\/1367497.1367510"},{"key":"22_CR21","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), pp. 3982\u20133992. Association for Computational Linguistics, Hong Kong, China (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1410, https:\/\/aclanthology.org\/D19-1410","DOI":"10.18653\/v1\/D19-1410"},{"key":"22_CR22","unstructured":"Su, J., Cao, J., Liu, W., Ou, Y.: Whitening sentence representations for better semantics and faster retrieval. arXiv preprint arXiv:2103.15316 (2021)"},{"key":"22_CR23","doi-asserted-by":"publisher","unstructured":"Williams, A., Nangia, N., Bowman, S.: A broad-coverage challenge corpus for sentence understanding through inference. In: Walker, M., Ji, H., Stent, A. (eds.) Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 1112\u20131122. Association for Computational Linguistics, New Orleans, Louisiana (2018). https:\/\/doi.org\/10.18653\/v1\/N18-1101, https:\/\/aclanthology.org\/N18-1101","DOI":"10.18653\/v1\/N18-1101"},{"key":"22_CR24","unstructured":"Wu, Z., Wang, S., Gu, J., Khabsa, M., Sun, F., Ma, H.: Clear: contrastive learning for sentence representation. arXiv preprint arXiv:2012.15466 (2020)"},{"key":"22_CR25","doi-asserted-by":"publisher","unstructured":"Xu, J., et al.: Self-taught convolutional neural networks for short text clustering. Neural Networks 88, 22\u201331 (2017). https:\/\/doi.org\/10.1016\/j.neunet.2016.12.008, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0893608016301976","DOI":"10.1016\/j.neunet.2016.12.008"},{"key":"22_CR26","doi-asserted-by":"publisher","unstructured":"Yan, Y., Li, R., Wang, S., Zhang, F., Wu, W., Xu, W.: ConSERT: a contrastive framework for self-supervised sentence representation transfer. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 5065\u20135075. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.393, https:\/\/aclanthology.org\/2021.acl-long.393","DOI":"10.18653\/v1\/2021.acl-long.393"},{"key":"22_CR27","doi-asserted-by":"publisher","unstructured":"Yin, J., Wang, J.: A model-based approach for text clustering with outlier detection. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 625\u2013636 (2016). https:\/\/doi.org\/10.1109\/ICDE.2016.7498276","DOI":"10.1109\/ICDE.2016.7498276"},{"key":"22_CR28","doi-asserted-by":"publisher","unstructured":"Zhang, D., et al.: Pairwise supervised contrastive learning of sentence representations. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 5786\u20135798. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.467, https:\/\/aclanthology.org\/2021.emnlp-main.467","DOI":"10.18653\/v1\/2021.emnlp-main.467"},{"key":"22_CR29","unstructured":"Zhang, X., LeCun, Y.: Text understanding from scratch. arXiv preprint arXiv:1502.01710 (2015)"},{"key":"22_CR30","doi-asserted-by":"crossref","unstructured":"Zhou, K., Zhang, B., Zhao, W.X., Wen, J.R.: Debiased contrastive learning of unsupervised sentence representations. arXiv preprint arXiv:2205.00656 (2022)","DOI":"10.18653\/v1\/2022.acl-long.423"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-2259-4_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T23:19:12Z","timestamp":1714000752000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2259-4_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819722617","9789819722594"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2259-4_22","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":"25 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taipei","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiwan","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":"7 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}