{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:08:30Z","timestamp":1743041310644,"version":"3.40.3"},"publisher-location":"Cham","reference-count":61,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031724367"},{"type":"electronic","value":"9783031724374"}],"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\/978-3-031-72437-4_16","type":"book-chapter","created":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T20:41:19Z","timestamp":1727296879000},"page":"273-291","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CALM: Context Augmentation with\u00a0Large Language Model for\u00a0Named Entity Recognition"],"prefix":"10.1007","author":[{"given":"Tristan","family":"Luiggi","sequence":"first","affiliation":[]},{"given":"Tanguy","family":"Herserant","sequence":"additional","affiliation":[]},{"given":"Thong","family":"Tran","sequence":"additional","affiliation":[]},{"given":"Laure","family":"Soulier","sequence":"additional","affiliation":[]},{"given":"Vincent","family":"Guigue","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,26]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","unstructured":"Bontcheva, K., Roberts, I., Derczynski, L., Rout, D.: The gate crowdsourcing plugin: crowdsourcing annotated corpora made easy. In: EACL 2014 - Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics, pp. 97\u2013100 (2014). https:\/\/doi.org\/10.3115\/V1\/E14-2025, https:\/\/aclanthology.org\/E14-2025","DOI":"10.3115\/V1\/E14-2025"},{"key":"16_CR2","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1162\/tacl_a_00104","volume":"4","author":"JP Chiu","year":"2016","unstructured":"Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Trans. Assoc. Comput. Linguist. 4, 357\u2013370 (2016)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"16_CR3","doi-asserted-by":"publisher","unstructured":"Cui, L., Wu, Y., Liu, J., Yang, S., Zhang, Y.: Template-based named entity recognition using BART. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 1835\u20131845, June 2021. https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.161, https:\/\/arxiv.org\/abs\/2106.01760v1","DOI":"10.18653\/v1\/2021.findings-acl.161"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Derczynski, L., Nichols, E., Van\u00a0Erp, M., Limsopatham, N.: Results of the WNUT2017 shared task on novel and emerging entity recognition. In: Proceedings of the 3rd Workshop on Noisy User-Generated Text, pp. 140\u2013147 (2017)","DOI":"10.18653\/v1\/W17-4418"},{"key":"16_CR5","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"16_CR6","doi-asserted-by":"publisher","unstructured":"Ding, N., et al.: Prompt-learning for fine-grained entity typing. In: Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 6917\u20136930, August 2021. https:\/\/doi.org\/10.18653\/v1\/2022.findings-emnlp.512, https:\/\/arxiv.org\/abs\/2108.10604v1","DOI":"10.18653\/v1\/2022.findings-emnlp.512"},{"key":"16_CR7","doi-asserted-by":"publisher","unstructured":"Hancock, B., Bringmann, M., Varma, P., Liang, P., Wang, S., R\u00e9, C.: Training classifiers with natural language explanations. In: ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), vol. 1, pp. 1884\u20131895 (2018). https:\/\/doi.org\/10.18653\/V1\/P18-1175, https:\/\/aclanthology.org\/P18-1175","DOI":"10.18653\/V1\/P18-1175"},{"key":"16_CR8","unstructured":"Hou, Y., Liu, Y., Che, W., Liu, T.: Sequence-to-sequence data augmentation for dialogue language understanding (2018). https:\/\/aclanthology.org\/C18-1105"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, Y., Liu, Y., Wan, X., Chang, T.H.: Hero-gang neural model for named entity recognition (2022)","DOI":"10.18653\/v1\/2022.naacl-main.140"},{"key":"16_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1007\/3-540-60925-3_51","volume-title":"Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing","author":"SB Huffman","year":"1996","unstructured":"Huffman, S.B.: Learning information extraction patterns from examples. In: Wermter, S., Riloff, E., Scheler, G. (eds.) IJCAI 1995. LNCS, vol. 1040, pp. 246\u2013260. Springer, Heidelberg (1996). https:\/\/doi.org\/10.1007\/3-540-60925-3_51"},{"key":"16_CR11","doi-asserted-by":"publisher","unstructured":"Iyyer, M., Wieting, J., Gimpel, K., Zettlemoyer, L.: Adversarial example generation with syntactically controlled paraphrase networks. In: NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, vol. 1, pp. 1875\u20131885 (2018). https:\/\/doi.org\/10.18653\/V1\/N18-1170, https:\/\/aclanthology.org\/N18-1170","DOI":"10.18653\/V1\/N18-1170"},{"key":"16_CR12","unstructured":"Jeong, M., Kang, J.: Regularizing models via pointwise mutual information for named entity recognition. CoRR abs\/2104.07249 (2021). https:\/\/arxiv.org\/abs\/2104.07249"},{"key":"16_CR13","unstructured":"Jeong, M., Kang, J.: Enhancing label consistency on document-level named entity recognition (2022)"},{"key":"16_CR14","unstructured":"Jiang, A.Q., et al.: Mistral 7B (2023)"},{"key":"16_CR15","doi-asserted-by":"publisher","unstructured":"Kobayashi, S.: Contextual augmentation: data augmentation by words with paradigmatic relations. In: NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, vol. 2, pp. 452\u2013457 (2018). https:\/\/doi.org\/10.18653\/V1\/N18-2072, https:\/\/aclanthology.org\/N18-2072","DOI":"10.18653\/V1\/N18-2072"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Kocaman, V., Talby, D.: Biomedical named entity recognition at scale (2020)","DOI":"10.1007\/978-3-030-68763-2_48"},{"key":"16_CR17","unstructured":"Kumar, V., Ai, A., Choudhary, A., Cho, E.: Data augmentation using pre-trained transformer models (2020). https:\/\/aclanthology.org\/2020.lifelongnlp-1.3"},{"key":"16_CR18","doi-asserted-by":"publisher","unstructured":"Kurata, G., Xiang, B., Zhou, B.: Labeled data generation with encoder-decoder LSTM for semantic slot filling. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 8\u201312 September 2016, pp. 725\u2013729 (2016). https:\/\/doi.org\/10.21437\/INTERSPEECH.2016-727","DOI":"10.21437\/INTERSPEECH.2016-727"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016)","DOI":"10.18653\/v1\/N16-1030"},{"key":"16_CR20","doi-asserted-by":"publisher","unstructured":"Lee, D.H., et al.: LEAN-LIFE: a label-efficient annotation framework towards learning from explanation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 372\u2013379 (2020). https:\/\/doi.org\/10.18653\/V1\/2020.ACL-DEMOS.42, https:\/\/aclanthology.org\/2020.acl-demos.42","DOI":"10.18653\/V1\/2020.ACL-DEMOS.42"},{"key":"16_CR21","doi-asserted-by":"publisher","unstructured":"Lee, D.H., et al.: AutoTrigger: label-efficient and robust named entity recognition with auxiliary trigger extraction. In: EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, pp. 3003\u20133017, September 2021. https:\/\/doi.org\/10.18653\/v1\/2023.eacl-main.219, https:\/\/arxiv.org\/abs\/2109.04726v3","DOI":"10.18653\/v1\/2023.eacl-main.219"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database 2016, baw068 (2016)","DOI":"10.1093\/database\/baw068"},{"key":"16_CR23","unstructured":"Li, X., Sun, X., Meng, Y., Liang, J., Wu, F., Li, J.: Dice loss for data-imbalanced NLP tasks. CoRR abs\/1911.02855 (2019). http:\/\/arxiv.org\/abs\/1911.02855"},{"key":"16_CR24","doi-asserted-by":"publisher","unstructured":"Lin, B.Y., et al.: TriggerNER: learning with entity triggers as explanations for named entity recognition. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 8503\u20138511 (2020). https:\/\/doi.org\/10.18653\/V1\/2020.ACL-MAIN.752, https:\/\/aclanthology.org\/2020.acl-main.752","DOI":"10.18653\/V1\/2020.ACL-MAIN.752"},{"issue":"9","key":"16_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560815","volume":"55","author":"P Liu","year":"2023","unstructured":"Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55(9), 1\u201335 (2023)","journal-title":"ACM Comput. Surv."},{"key":"16_CR26","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach (2019). arXiv preprint arXiv:1907.11692, vol. 364 (2019)"},{"key":"16_CR27","unstructured":"Loshchilov, I., Hutter, F.: Fixing weight decay regularization in adam (2018)"},{"key":"16_CR28","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. CoRR abs\/1310.4546 (2013). http:\/\/arxiv.org\/abs\/1310.4546"},{"key":"16_CR29","doi-asserted-by":"publisher","unstructured":"Min, J., McCoy, R.T., Das, D., Pitler, E., Linzen, T.: Syntactic data augmentation increases robustness to inference heuristics. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 2339\u20132352 (2020). https:\/\/doi.org\/10.18653\/V1\/2020.ACL-MAIN.212, https:\/\/aclanthology.org\/2020.acl-main.212","DOI":"10.18653\/V1\/2020.ACL-MAIN.212"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Morton, T.S., LaCivita, J.: WordFreak: an open tool for linguistic annotation (2003). https:\/\/aclanthology.org\/N03-4009","DOI":"10.3115\/1073427.1073436"},{"key":"16_CR31","unstructured":"OpenAI: GPT-4 technical report (2023)"},{"key":"16_CR32","doi-asserted-by":"crossref","unstructured":"Peters, M.E., et al.: Deep contextualized word representations (2018)","DOI":"10.18653\/v1\/N18-1202"},{"key":"16_CR33","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. CoRR abs\/1910.10683 (2019). http:\/\/arxiv.org\/abs\/1910.10683"},{"key":"16_CR34","doi-asserted-by":"crossref","unstructured":"Ramshaw, L.A., Marcus, M.P.: Text chunking using transformation-based learning. In: Natural Language Processing Using Very Large Corpora, pp. 157\u2013176 (1999)","DOI":"10.1007\/978-94-017-2390-9_10"},{"key":"16_CR35","unstructured":"Rei, M.: Semi-supervised multitask learning for sequence labeling. CoRR abs\/1704.07156 (2017). http:\/\/arxiv.org\/abs\/1704.07156"},{"key":"16_CR36","unstructured":"Sang, E.F., De\u00a0Meulder, F.: Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. arXiv preprint cs\/0306050 (2003)"},{"key":"16_CR37","doi-asserted-by":"publisher","unstructured":"Seyler, D., Dembelova, T., Del\u00a0Corro, L., Hoffart, J., Weikum, G.: A study of the importance of external knowledge in the named entity recognition task. In: Gurevych, I., Miyao, Y. (eds.) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 241\u2013246. Association for Computational Linguistics, Melbourne, Australia, July 2018. https:\/\/doi.org\/10.18653\/v1\/P18-2039, https:\/\/aclanthology.org\/P18-2039","DOI":"10.18653\/v1\/P18-2039"},{"key":"16_CR38","unstructured":"Singh, T.D., Nongmeikapam, K., Ekbal, A., Bandyopadhyay, S.: Named entity recognition for Manipuri using support vector machine. In: Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, vol. 2, pp. 811\u2013818 (2009)"},{"key":"16_CR39","doi-asserted-by":"publisher","unstructured":"Srivastava, S., Labutov, I., Mitchell, T.: Joint concept learning and semantic parsing from natural language explanations. In: EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings, pp. 1527\u20131536 (2017). https:\/\/doi.org\/10.18653\/V1\/D17-1161, https:\/\/aclanthology.org\/D17-1161","DOI":"10.18653\/V1\/D17-1161"},{"key":"16_CR40","unstructured":"Stenetorp, P., Pyysalo, S., Topi\u0107, G., Ohta, T., Ananiadou, S., Tsujii, J.: BRAT: a web-based tool for NLP-assisted text annotation (2012). https:\/\/aclanthology.org\/E12-2021"},{"key":"16_CR41","unstructured":"Sutton, C., McCallum, A.: An introduction to conditional random fields (2010)"},{"key":"16_CR42","doi-asserted-by":"publisher","unstructured":"Taill\u00e9, B., Guigue, V., Gallinari, P.: Contextualized embeddings in named-entity recognition: an empirical study on generalization. In: Jose, J., et al. (eds.) Advances in Information Retrieval: 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, 14\u201317 April 2020, Proceedings, Part II 42, pp. 383\u2013391. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-45442-5_48","DOI":"10.1007\/978-3-030-45442-5_48"},{"key":"16_CR43","unstructured":"Touvron, H., et\u00a0al.: LLaMA 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)"},{"key":"16_CR44","doi-asserted-by":"publisher","unstructured":"Ushio, A., Camacho-Collados, J.: T-NER: an all-round python library for transformer-based named entity recognition. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.eacl-demos.7","DOI":"10.18653\/v1\/2021.eacl-demos.7"},{"key":"16_CR45","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"16_CR46","unstructured":"Wang, S., et al.: GPT-NER: named entity recognition via large language models. arXiv preprint arXiv:2304.10428 (2023)"},{"key":"16_CR47","unstructured":"Wang, X., et al.: Automated concatenation of embeddings for structured prediction. CoRR abs\/2010.05006 (2020). https:\/\/arxiv.org\/abs\/2010.05006"},{"key":"16_CR48","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Improving named entity recognition by external context retrieving and cooperative learning. arXiv preprint arXiv:2105.03654 (2021)","DOI":"10.18653\/v1\/2021.acl-long.142"},{"key":"16_CR49","doi-asserted-by":"crossref","unstructured":"Wang, Z., Shang, J., Liu, L., Lu, L., Liu, J., Han, J.: CrossWeigh: training named entity tagger from imperfect annotations. CoRR abs\/1909.01441 (2019). http:\/\/arxiv.org\/abs\/1909.01441","DOI":"10.18653\/v1\/D19-1519"},{"key":"16_CR50","unstructured":"Wang*, Z., et al.: Learning from explanations with neural execution tree, September 2019. http:\/\/inklab.usc.edu\/project-NExT\/"},{"key":"16_CR51","doi-asserted-by":"publisher","unstructured":"Wei, J., Zou, K.: EDA: easy data augmentation techniques for boosting performance on text classification tasks. In: EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, pp. 6382\u20136388 (2019). https:\/\/doi.org\/10.18653\/V1\/D19-1670, https:\/\/aclanthology.org\/D19-1670","DOI":"10.18653\/V1\/D19-1670"},{"key":"16_CR52","unstructured":"White, J., et al.: A prompt pattern catalog to enhance prompt engineering with ChatGPT. arXiv preprint arXiv:2302.11382 (2023)"},{"key":"16_CR53","doi-asserted-by":"publisher","unstructured":"Wu, X., Lv, S., Zang, L., Han, J., Hu, S.: Conditional BERT contextual augmentation. In: Rodrigues, J., et al. (eds.) ICCS 2019. LNCS (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11539, pp. 84\u201395. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-22747-0_7, https:\/\/arxiv.org\/abs\/1812.06705v1","DOI":"10.1007\/978-3-030-22747-0_7"},{"key":"16_CR54","doi-asserted-by":"publisher","unstructured":"Yamada, I., Asai, A., Shindo, H., Takeda, H., Matsumoto, Y.: LUKE: deep contextualized entity representations with entity-aware self-attention. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6442\u20136454. Association for Computational Linguistics, Online, November 2020. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.523, https:\/\/aclanthology.org\/2020.emnlp-main.523","DOI":"10.18653\/v1\/2020.emnlp-main.523"},{"key":"16_CR55","doi-asserted-by":"publisher","unstructured":"Yang, J., Zhang, Y., Li, L., Li, X.: YEDDA: a lightweight collaborative text span annotation tool. In: ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of System Demonstrations, pp. 31\u201336 (2018). https:\/\/doi.org\/10.18653\/V1\/P18-4006, https:\/\/aclanthology.org\/P18-4006","DOI":"10.18653\/V1\/P18-4006"},{"key":"16_CR56","unstructured":"Yu, A.W., et al.: QANet: combining local convolution with global self-attention for reading comprehension. In: 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings, April 2018. https:\/\/arxiv.org\/abs\/1804.09541v1"},{"key":"16_CR57","unstructured":"Zhang, S., Cheng, H., Gao, J., Poon, H.: Optimizing bi-encoder for named entity recognition via contrastive learning (2023)"},{"key":"16_CR58","unstructured":"Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: evaluating text generation with BERT. arXiv preprint arXiv:1904.09675 (2019)"},{"key":"16_CR59","unstructured":"Zhang, X., Zhao, J., Lecun, Y.: Character-level convolutional networks for text classification (2015)"},{"key":"16_CR60","doi-asserted-by":"publisher","unstructured":"Zhou, W., Chen, M.: Learning from noisy labels for entity-centric information extraction. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.437","DOI":"10.18653\/v1\/2021.emnlp-main.437"},{"key":"16_CR61","doi-asserted-by":"publisher","unstructured":"Zhou, W., et al.: NERO: a neural rule grounding framework for label-efficient relation extraction. In: The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020, pp. 2166\u20132176, September 2019. https:\/\/doi.org\/10.1145\/3366423.3380282, https:\/\/arxiv.org\/abs\/1909.02177v4","DOI":"10.1145\/3366423.3380282"}],"container-title":["Lecture Notes in Computer Science","Linking Theory and Practice of Digital Libraries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72437-4_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T05:05:08Z","timestamp":1731647108000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72437-4_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031724367","9783031724374"],"references-count":61,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72437-4_16","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":"26 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"TPDL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Theory and Practice of Digital Libraries","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ljubljana","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovenia","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":"24 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 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":"tpdl2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/tpdl2024.nuk.si\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}