{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T15:36:26Z","timestamp":1777476986266,"version":"3.51.4"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031901669","type":"print"},{"value":"9783031901676","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-90167-6_23","type":"book-chapter","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T02:15:00Z","timestamp":1745288100000},"page":"339-356","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["EmotioNotes Dataset: Decoding Emotions in\u00a0Classical Music Through Concert Program Notes"],"prefix":"10.1007","author":[{"given":"Pratik","family":"Khanal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick J.","family":"Donnelly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,20]]},"reference":[{"key":"23_CR1","doi-asserted-by":"publisher","unstructured":"Agrawal, Y., Shanker, R., Alluri, V.: Transformer-based approach towards music emotion recognition from lyrics. In: Hiemstra, D., Moens, M.-F., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds.) ECIR 2021. LNCS, vol. 12657, pp. 167\u2013175. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72240-1_12","DOI":"10.1007\/978-3-030-72240-1_12"},{"issue":"3","key":"23_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0173392","volume":"12","author":"A Aljanaki","year":"2017","unstructured":"Aljanaki, A., Yang, Y.H., Soleymani, M.: Developing a benchmark for emotional analysis of music. PLoS ONE 12(3), 1\u201322 (2017). https:\/\/doi.org\/10.1371\/journal.pone.0173392","journal-title":"PLoS ONE"},{"issue":"3","key":"23_CR3","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1353\/not.2008.0023","volume":"64","author":"C Bashford","year":"2008","unstructured":"Bashford, C.: Writing (british) concert history: the blessing and curse of ephemera. Notes 64(3), 458\u2013473 (2008). https:\/\/doi.org\/10.1353\/not.2008.0023","journal-title":"Notes"},{"key":"23_CR4","doi-asserted-by":"publisher","unstructured":"Beery, A., Donnelly, P.J.: Learning affective responses to music from social media discourse, pp. 93\u2013119. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-44260-5_6","DOI":"10.1007\/978-3-031-44260-5_6"},{"key":"23_CR5","doi-asserted-by":"publisher","unstructured":"Beltagy, I., Peters, M.E., Cohan, A.: Longformer: the long-document transformer. arXiv preprint arXiv:2004.05150 (2020). https:\/\/doi.org\/10.48550\/arXiv.2004.05150","DOI":"10.48550\/arXiv.2004.05150"},{"key":"23_CR6","doi-asserted-by":"publisher","unstructured":"Blom, D.M., Bennett, D., Stevenson, I.: The composer\u2019s program note for newly written classical music: content and intentions. Front. Psychol. 7 (2016). https:\/\/doi.org\/10.3389\/fpsyg.2016.01707","DOI":"10.3389\/fpsyg.2016.01707"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Bower, B.: The routledge companion to applied musicology, chap. George Grove, Programme Notes, and the Dictionary, 46\u201354. Taylor & Francis (2023)","DOI":"10.4324\/9781003042983-6"},{"key":"23_CR8","unstructured":"Bradley, M., Lang, P.: Affective Norms for English Words (ANEW): instruction manual and affective ratings (1999). https:\/\/www.semanticscholar.org\/paper\/Affective-Norms-for-English-Words-(ANEW)%3A-Manual-Bradley-Lang\/c765eb0a31849361d829b24e173a37bab0919892"},{"issue":"4","key":"23_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0266323","volume":"17","author":"J Carney","year":"2022","unstructured":"Carney, J., Robertson, C.: Five studies evaluating the impact on mental health and mood of recalling, reading, and discussing fiction. PLoS ONE 17(4), 1\u201332 (2022). https:\/\/doi.org\/10.1371\/journal.pone.0266323","journal-title":"PLoS ONE"},{"issue":"1","key":"23_CR10","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s00530-021-00786-6","volume":"28","author":"V Chaturvedi","year":"2021","unstructured":"Chaturvedi, V., Kaur, A.B., Varshney, V., Garg, A., Chhabra, G.S., Kumar, M.: Music mood and human emotion recognition based on physiological signals: a systematic review. Multimedia Syst. 28(1), 21\u201344 (2021). https:\/\/doi.org\/10.1007\/s00530-021-00786-6","journal-title":"Multimedia Syst."},{"key":"23_CR11","doi-asserted-by":"publisher","unstructured":"Chen, Y.A., Yang, Y.H., Wang, J.C., Chen, H.: The AMG1608 dataset for music emotion recognition. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 693\u2013697. IEEE (2015). https:\/\/doi.org\/10.1109\/ICASSP.2015.7178058","DOI":"10.1109\/ICASSP.2015.7178058"},{"key":"23_CR12","doi-asserted-by":"publisher","unstructured":"Delbouys, R., Hennequin, R., Piccoli, F., Royo-Letelier, J., Moussallam, M.: Music mood detection based on audio and lyrics with deep neural net. In: Proceedings of the 19th International Society for Music Information Retrieval Conference, pp. 370\u2013375. ISMIR (2018). https:\/\/doi.org\/10.5281\/zenodo.1492427","DOI":"10.5281\/zenodo.1492427"},{"key":"23_CR13","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 naacL-HLT. vol.\u00a01, pp. 4171\u20134186. Association for Computational Linguistic (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"23_CR14","doi-asserted-by":"publisher","unstructured":"Divekar, A., Durrett, G.: SynthesizRR: generating diverse datasets with retrieval augmentation. arXiv preprint arXiv:2405.10040 (2024). https:\/\/doi.org\/10.48550\/arXiv.2405.10040","DOI":"10.48550\/arXiv.2405.10040"},{"key":"23_CR15","unstructured":"Donnelly, P., Beery, A.: Evaluating large-language models for dimensional music emotion prediction from social media discourse. In: Abbas, M., Freihat, A.A. (eds.) Proceedings of the 5th International Conference on Natural Language and Speech Processing (ICNLSP 2022), pp. 242\u2013250. Association for Computational Linguistics, Trento, Italy (2022)"},{"key":"23_CR16","doi-asserted-by":"publisher","unstructured":"Ehrmann, M., Hamdi, A., Pontes, E.L., Romanello, M., Doucet, A.: Named entity recognition and classification in historical documents: a survey. ACM Comput. Surv. 56(2), 27:1\u201327:47 (2023). https:\/\/doi.org\/10.1145\/3604931","DOI":"10.1145\/3604931"},{"key":"23_CR17","doi-asserted-by":"publisher","unstructured":"Feng, H., Mahoor, M.H., Dino, F.: A music-therapy robotic platform for children with autism: a pilot study. Front. Robot. AI 9 (2022). https:\/\/doi.org\/10.3389\/frobt.2022.855819","DOI":"10.3389\/frobt.2022.855819"},{"key":"23_CR18","doi-asserted-by":"publisher","unstructured":"Gaur, S., Donnelly, P.J.: Generating smooth mood-dynamic playlists with audio features and KNN. In: Johnson, C., Rebelo, S.M., Santos, I. (eds.) Proceedings of the 13th International Conference of Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMUSART). vol. 14633, pp. 162\u2013178. Springer (2024). https:\/\/doi.org\/10.1007\/978-3-031-56992-0_11","DOI":"10.1007\/978-3-031-56992-0_11"},{"key":"23_CR19","doi-asserted-by":"publisher","unstructured":"Guo, M., et al.: Longt5: efficient text-to-text transformer for long sequences. In: Carpuat, M., de\u00a0Marneffe, M.C., Meza\u00a0Ruiz, I.V. (eds.) Findings of the Association for Computational Linguistics: NAACL 2022, pp. 724\u2013736. Association for Computational Linguistics, Seattle, United States (2022). https:\/\/doi.org\/10.18653\/v1\/2022.findings-naacl.55","DOI":"10.18653\/v1\/2022.findings-naacl.55"},{"issue":"6","key":"23_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11704-021-0569-4","volume":"16","author":"D Han","year":"2022","unstructured":"Han, D., Kong, Y., Han, J., Wang, G.: A survey of music emotion recognition. Front. Comp. Sci. 16(6), 1\u201311 (2022). https:\/\/doi.org\/10.1007\/s11704-021-0569-4","journal-title":"Front. Comp. Sci."},{"key":"23_CR21","doi-asserted-by":"publisher","unstructured":"He, N., Ferguson, S.: Multi-view neural networks for raw audio-based music emotion recognition. In: 2020 IEEE International Symposium on Multimedia (ISM), pp. 168\u2013172 (2020). https:\/\/doi.org\/10.1109\/ISM.2020.00037","DOI":"10.1109\/ISM.2020.00037"},{"issue":"3","key":"23_CR22","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1016\/j.jestch.2020.10.009","volume":"24","author":"S Hizlisoy","year":"2021","unstructured":"Hizlisoy, S., Yildirim, S., Tufekci, Z.: Music emotion recognition using convolutional long short term memory deep neural networks. Eng. Sci. Technol. Int. J. 24(3), 760\u2013767 (2021). https:\/\/doi.org\/10.1016\/j.jestch.2020.10.009","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"23_CR23","doi-asserted-by":"publisher","unstructured":"Hung, H.T., Ching, J., Doh, S., Kim, N., Nam, J., Yang, Y.H.: EMOPIA: a multi-modal pop piano dataset for emotion recognition and emotion-based music generation. In: Proceedings International Society for Music Information Retrieval Conferences, pp. 318-325. ISMIR, Online (2021). https:\/\/doi.org\/10.5281\/zenodo.5624519","DOI":"10.5281\/zenodo.5624519"},{"key":"23_CR24","doi-asserted-by":"publisher","unstructured":"Ji, Z., et al.: Survey of hallucination in natural language generation. ACM Comput. Surv. 55(12) (2023). https:\/\/doi.org\/10.1145\/3571730","DOI":"10.1145\/3571730"},{"issue":"3","key":"23_CR25","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1109\/TBDATA.2019.2921572","volume":"7","author":"J Johnson","year":"2021","unstructured":"Johnson, J., Douze, M., J\u00e9gou, H.: Billion-scale similarity search with GPUs. IEEE Trans. Big Data 7(3), 535\u2013547 (2021). https:\/\/doi.org\/10.1109\/TBDATA.2019.2921572","journal-title":"IEEE Trans. Big Data"},{"key":"23_CR26","doi-asserted-by":"publisher","unstructured":"Kitaev, N., Kaiser, L., Levskaya, A.: Reformer: the efficient transformer (2019). https:\/\/doi.org\/10.48550\/arXiv.2001.04451","DOI":"10.48550\/arXiv.2001.04451"},{"key":"23_CR27","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 9459\u20139474. Curran Associates, Inc. (2020)"},{"key":"23_CR28","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. CoRR abs\/1907.11692 (2019). http:\/\/arxiv.org\/abs\/1907.11692"},{"issue":"3","key":"23_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103256","volume":"60","author":"Z Liu","year":"2023","unstructured":"Liu, Z., Xu, W., Zhang, W., Jiang, Q.: An emotion-based personalized music recommendation framework for emotion improvement. Inform. Process. Manage. 60(3), 103256 (2023). https:\/\/doi.org\/10.1016\/j.ipm.2022.103256","journal-title":"Inform. Process. Manage."},{"issue":"3","key":"23_CR30","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1177\/0305735609351921","volume":"38","author":"EH Margulis","year":"2010","unstructured":"Margulis, E.H.: When program notes don\u2019t help: music descriptions and enjoyment. Psychol. Music 38(3), 285\u2013302 (2010). https:\/\/doi.org\/10.1177\/0305735609351921","journal-title":"Psychol. Music"},{"key":"23_CR31","volume-title":"An Approach to Environmental Psychology","author":"A Mehrabian","year":"1974","unstructured":"Mehrabian, A., Russell, J.A.: An Approach to Environmental Psychology. The MIT Press, Cambridge (1974)"},{"key":"23_CR32","doi-asserted-by":"publisher","unstructured":"Nguyen, T.T.H., Jatowt, A., Coustaty, M., Doucet, A.: Survey of post-ocr processing approaches. ACM Comput. Surv. 54(6), 124:1\u2013124:37 (2021). https:\/\/doi.org\/10.1145\/3453476","DOI":"10.1145\/3453476"},{"issue":"4","key":"23_CR33","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1109\/TAFFC.2018.2820691","volume":"11","author":"R Panda","year":"2020","unstructured":"Panda, R., Malheiro, R., Paiva, R.P.: Novel audio features for music emotion recognition. IEEE Trans. Affect. Comput. 11(4), 614\u2013626 (2020). https:\/\/doi.org\/10.1109\/TAFFC.2018.2820691","journal-title":"IEEE Trans. Affect. Comput."},{"key":"23_CR34","unstructured":"Pasler, J.: Concert programs and their narratives as emblems of ideology. Int. J. Musicology, 249\u2013308 (1993)"},{"key":"23_CR35","doi-asserted-by":"publisher","unstructured":"Plutchik, R.: A general psychoevolutionary theory of emotion. In: Plutchik, R., Kellerman, H. (eds.) Theories of Emotion, pp. 3\u201333. Academic Press (1980). https:\/\/doi.org\/10.1016\/B978-0-12-558701-3.50007-7","DOI":"10.1016\/B978-0-12-558701-3.50007-7"},{"key":"23_CR36","doi-asserted-by":"publisher","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) 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 (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1410","DOI":"10.18653\/v1\/D19-1410"},{"issue":"3","key":"23_CR37","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1162\/coli_a_00322","volume":"44","author":"E Reiter","year":"2018","unstructured":"Reiter, E.: A structured review of the validity of BLEU. Comput. Linguist. 44(3), 393\u2013401 (2018). https:\/\/doi.org\/10.1162\/coli_a_00322","journal-title":"Comput. Linguist."},{"key":"23_CR38","unstructured":"Ridgewell, R.: The concert programmes project: history, progress and future directions. Fontes Artis Musicae, 50\u201364 (2010)"},{"issue":"6","key":"23_CR39","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1037\/h0077714","volume":"39","author":"JA Russell","year":"1980","unstructured":"Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161\u20131178 (1980). https:\/\/doi.org\/10.1037\/h0077714","journal-title":"J. Pers. Soc. Psychol."},{"key":"23_CR40","doi-asserted-by":"publisher","unstructured":"Touvron, H., et\u00a0al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023). https:\/\/doi.org\/10.48550\/arXiv.2302.13971","DOI":"10.48550\/arXiv.2302.13971"},{"key":"23_CR41","doi-asserted-by":"publisher","unstructured":"Vamvakas, G., Gatos, B., Stamatopoulos, N., Perantonis, S.: A complete optical character recognition methodology for historical documents. In: 2008 The Eighth IAPR International Workshop on Document Analysis Systems, pp. 525\u2013532 (2008). https:\/\/doi.org\/10.1109\/DAS.2008.73","DOI":"10.1109\/DAS.2008.73"},{"key":"23_CR42","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000\u20136010. NIPS\u201917, Curran Associates Inc., Red Hook, NY (2017)"},{"issue":"7","key":"23_CR43","doi-asserted-by":"publisher","first-page":"5731","DOI":"10.1007\/s10462-022-10144-1","volume":"55","author":"M Wankhade","year":"2022","unstructured":"Wankhade, M., Rao, A., Kulkarni, C.: A survey on sentiment analysis methods, applications, and challenges. Artif. Intell. Rev. 55(7), 5731\u20135780 (2022). https:\/\/doi.org\/10.1007\/s10462-022-10144-1","journal-title":"Artif. Intell. Rev."},{"issue":"4","key":"23_CR44","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.3758\/s13428-012-0314-x","volume":"45","author":"AB Warriner","year":"2013","unstructured":"Warriner, A.B., Kuperman, V., Brysbaert, M.: Norms of valence, arousal, and dominance for 13,915 english lemmas. Behav. Res. Methods 45(4), 1191\u20131207 (2013). https:\/\/doi.org\/10.3758\/s13428-012-0314-x","journal-title":"Behav. Res. Methods"},{"key":"23_CR45","doi-asserted-by":"publisher","DOI":"10.1093\/gmo\/9781561592630.article.06240","author":"W Weber","year":"2001","unstructured":"Weber, W.: Concert (ii). Grove Music Online (2001). https:\/\/doi.org\/10.1093\/gmo\/9781561592630.article.06240","journal-title":"Grove Music Online"},{"key":"23_CR46","doi-asserted-by":"publisher","unstructured":"Xiong, Y., et al.: Nystr\u00f6mformer: a nystr\u00f6m-based algorithm for approximating self-attention. In: Proceedings of the AAAI Conference on Artificial Intelligence vol. 35, no. 1616, pp. 14138\u201314148 (2021). https:\/\/doi.org\/10.1609\/aaai.v35i16.17664","DOI":"10.1609\/aaai.v35i16.17664"},{"key":"23_CR47","unstructured":"Zaheer, M.: Big bird: transformers for longer sequences. In: Proceedings of the 34th International Conference on Neural Information Processing Systems, pp. 17283\u201317297. NIPS \u201920, Curran Associates Inc., Red Hook, NY (2020)"},{"key":"23_CR48","doi-asserted-by":"publisher","unstructured":"Zhang, K., Zhang, H., Li, S., Yang, C., Sun, L.: The PMEmo dataset for music emotion recognition. In: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, pp. 135\u2013142. ICMR \u201918, ACM, New York (2018). https:\/\/doi.org\/10.1145\/3206025.3206037","DOI":"10.1145\/3206025.3206037"},{"key":"23_CR49","doi-asserted-by":"publisher","unstructured":"Zhou, C., et al.: A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT. arXiv preprint arXiv:2302.09419 (2023). https:\/\/doi.org\/10.48550\/arXiv.2302.09419","DOI":"10.48550\/arXiv.2302.09419"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Music, Sound, Art and Design"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-90167-6_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T02:15:13Z","timestamp":1745288113000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-90167-6_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031901669","9783031901676"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-90167-6_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"20 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EvoMUSART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Trieste","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"evomusart2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.evostar.org\/2025\/evomusart\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}