{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T11:05:04Z","timestamp":1742987104806,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031702389"},{"type":"electronic","value":"9783031702396"}],"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\/978-3-031-70239-6_25","type":"book-chapter","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T06:02:09Z","timestamp":1726725729000},"page":"364-378","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Shact: Disentangling and\u00a0Clustering Latent Syntactic Structures from\u00a0Transformer Encoders"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3637-4904","authenticated-orcid":false,"given":"Alejandro","family":"Sierra-M\u00fanera","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5036-8589","authenticated-orcid":false,"given":"Ralf","family":"Krestel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,20]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Chen, L., Liu, X., Ruan, W., Lu, J.: Enhance robustness of sequence labelling with masked adversarial training. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 297\u2013302 (2020)","key":"25_CR1","DOI":"10.18653\/v1\/2020.findings-emnlp.28"},{"doi-asserted-by":"crossref","unstructured":"Chen, L., Ruan, W., Liu, X., Lu, J.: SeqVAT: virtual adversarial training for semi-supervised sequence labeling. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8801\u20138811 (2020)","key":"25_CR2","DOI":"10.18653\/v1\/2020.acl-main.777"},{"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, pp. 4171\u20134186 (2019)","key":"25_CR3"},{"doi-asserted-by":"crossref","unstructured":"Fu, Y., Tan, C., Chen, M., Huang, S., Huang, F.: Nested named entity recognition with partially-observed TreeCRFs. IN: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35(14), pp. 12839\u201312847 (2021)","key":"25_CR4","DOI":"10.1609\/aaai.v35i14.17519"},{"unstructured":"Hewitt, J., Manning, C.D.: A structural probe for finding syntax in word representations. 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. 4129\u20134138 (2019)","key":"25_CR5"},{"key":"25_CR6","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2019","unstructured":"Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36, 1234\u20131240 (2019)","journal-title":"Bioinformatics"},{"doi-asserted-by":"crossref","unstructured":"Lou, C., Yang, S., Tu, K.: Nested named entity recognition as latent lexicalized constituency parsing. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 6183\u20136198 (2022)","key":"25_CR7","DOI":"10.18653\/v1\/2022.acl-long.428"},{"doi-asserted-by":"crossref","unstructured":"Mare\u010dek, D., Rosa, R.: From balustrades to pierre vinken: looking for syntax in transformer self-attentions. In: Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pp. 263\u2013275 (2019)","key":"25_CR8","DOI":"10.18653\/v1\/W19-4827"},{"doi-asserted-by":"crossref","unstructured":"Ohta, T., Tateisi, Y., Kim, J.D.: The Genia corpus: an annotated research abstract corpus in molecular biology domain. In: Proceedings of the Second International Conference on Human Language Technology Research, pp. 8286, HLT \u201902, San Francisco, CA, USA (2002)","key":"25_CR9","DOI":"10.3115\/1289189.1289260"},{"unstructured":"Radford, A., Narasimhan, K.: Improving language understanding by generative pre-training (2018)","key":"25_CR10"},{"doi-asserted-by":"crossref","unstructured":"Sajjad, H., Durrani, N., Dalvi, F., Alam, F., Khan, A., Xu, J.: Analyzing encoded concepts in transformer language models. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 3082\u20133101 (2022)","key":"25_CR11","DOI":"10.18653\/v1\/2022.naacl-main.225"},{"doi-asserted-by":"crossref","unstructured":"Shen, Y., Song, K., Tan, X., Li, D., Lu, W., Zhuang, Y.: DiffusionNER: boundary diffusion for named entity recognition. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3875\u20133890 (2023)","key":"25_CR12","DOI":"10.18653\/v1\/2023.acl-long.215"},{"doi-asserted-by":"crossref","unstructured":"Shen, Y., et al.: Parallel instance query network for named entity recognition. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 947\u2013961 (2022)","key":"25_CR13","DOI":"10.18653\/v1\/2022.acl-long.67"},{"doi-asserted-by":"crossref","unstructured":"Sohrab, M.G., Miwa, M.: Deep exhaustive model for nested named entity recognition. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2843\u20132849 (2018)","key":"25_CR14","DOI":"10.18653\/v1\/D18-1309"},{"doi-asserted-by":"crossref","unstructured":"Tan, C., Qiu, W., Chen, M., Wang, R., Huang, F.: Boundary enhanced neural span classification for nested named entity recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34(05), pp. 9016\u20139023 (2020)","key":"25_CR15","DOI":"10.1609\/aaai.v34i05.6434"},{"doi-asserted-by":"crossref","unstructured":"Tjong Kim\u00a0Sang, E.F., Buchholz, S.: Introduction to the CoNLL-2000 shared task chunking. In: Fourth Conference on Computational Natural Language Learning and the Second Learning Language in Logic Workshop (2000)","key":"25_CR16","DOI":"10.3115\/1117601.1117631"},{"doi-asserted-by":"crossref","unstructured":"Tjong Kim\u00a0Sang, E.F., De\u00a0Meulder, F.: Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, pp. 142\u2013147 (2003)","key":"25_CR17","DOI":"10.3115\/1119176.1119195"},{"unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 60006010, NIPS\u201917 (2017)","key":"25_CR18"},{"doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Automated concatenation of embeddings for structured prediction. 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. 2643\u20132660 (2021)","key":"25_CR19","DOI":"10.18653\/v1\/2021.acl-long.206"},{"doi-asserted-by":"crossref","unstructured":"Wu, Z., Chen, Y., Kao, B., Liu, Q.: Perturbed masking: Parameter-free probing for analyzing and interpreting BERT. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 4166\u20134176 (2020)","key":"25_CR20","DOI":"10.18653\/v1\/2020.acl-main.383"},{"doi-asserted-by":"crossref","unstructured":"Yang, S., Tu, K.: Bottom-up constituency parsing and nested named entity recognition with pointer networks. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2403\u20132416 (2022)","key":"25_CR21","DOI":"10.18653\/v1\/2022.acl-long.171"},{"doi-asserted-by":"crossref","unstructured":"Zheng, C., Cai, Y., Xu, J., Leung, H.F., Xu, G.: A boundary-aware neural model for nested named entity recognition. 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. 357\u2013366 (2019)","key":"25_CR22","DOI":"10.18653\/v1\/D19-1034"}],"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\/978-3-031-70239-6_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T06:06:40Z","timestamp":1726726000000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70239-6_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031702389","9783031702396"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70239-6_25","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":"20 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"25 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nldb2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/nldb2024.di.unito.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}