{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T07:08:12Z","timestamp":1768115292012,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819544448","type":"print"},{"value":"9789819544455","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-4445-5_11","type":"book-chapter","created":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T03:44:19Z","timestamp":1768103059000},"page":"151-165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MetaCRN: Language-Augmented Multimodal Metaphor Detection Using Cross-Modal Dynamic Replacement"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7171-9642","authenticated-orcid":false,"given":"Hao","family":"Meng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5007-8805","authenticated-orcid":false,"given":"Qimeng","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3108-8125","authenticated-orcid":false,"given":"Jingwen","family":"Ma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3333-470X","authenticated-orcid":false,"given":"Qixing","family":"Wei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"issue":"1","key":"11_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2024.103946","volume":"62","author":"D Wang","year":"2025","unstructured":"Wang, D., et al.: CKEMI: Concept knowledge enhanced metaphor identification framework. Inf. Process. Manag. 62(1), 103946 (2025)","journal-title":"Inf. Process. Manag."},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Steen, G., et al.: A method for linguistic metaphor identification from MIP to MIPVU preface. Method for linguistic metaphor identification: from MIP To MIPVU 14 (2010): IX-+","DOI":"10.1075\/celcr.14"},{"key":"11_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111778","volume":"294","author":"B Wang","year":"2024","unstructured":"Wang, B., et al.: What do they \u201cmeme\u2019\u2019? A metaphor-aware multi-modal multi-task framework for fine-grained meme understanding. Knowl. Based Syst. 294, 111778 (2024)","journal-title":"Knowl. Based Syst."},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Zhang, L., et al.: CAMEL: capturing metaphorical alignment with context disentangling for multimodal emotion recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38. No. 8 (2024)","DOI":"10.1609\/aaai.v38i8.28787"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Mao, R., et al.: MetaPro 2.0: computational metaphor processing on the effectiveness of anomalous language modeling. Find. Assoc. Comput. Linguist. ACL 2024 (2024)","DOI":"10.18653\/v1\/2024.findings-acl.590"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, S., Liu, Y.: Adversarial multi-task learning for end-to-end metaphor detection. arXiv preprint arXiv:2305.16638 (2023)","DOI":"10.18653\/v1\/2023.findings-acl.96"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Badathala, N., et al.: A match made in heaven: a multi-task framework for hyperbole and metaphor detection. arXiv preprint arXiv:2305.17480 (2023)","DOI":"10.18653\/v1\/2023.findings-acl.26"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Mao, R., et al.: MetaPro: a computational metaphor processing model for text pre-processing. Inf. Fus. 86, 30\u201343 (2022)","DOI":"10.1016\/j.inffus.2022.06.002"},{"key":"11_CR9","unstructured":"Zhang, S., Liu. Y.: Metaphor detection via linguistics enhanced Siamese network. In: Proceedings of the 29th international conference on computational linguistics (2022)"},{"key":"11_CR10","unstructured":"Wang, S., et al.: Metaphor detection with effective context denoising. arXiv preprint arXiv:2302.05611 (2023)"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Zheng, L., et al.: Multi-granular multimodal clue fusion for meme understanding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, No. 24 (2025)","DOI":"10.1609\/aaai.v39i24.34801"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Shutova, E., Kiela, D., Maillard. J.: Black holes and white rabbits: metaphor identification with visual features. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Human language technologies (2016)","DOI":"10.18653\/v1\/N16-1020"},{"key":"11_CR13","unstructured":"Kehat, G., Pustejovsky, J.: Improving neural metaphor detection with visual datasets. In: Proceedings of the twelfth language resources and evaluation conference (2020)"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Su, C., et al.: Multimodal metaphor detection based on distinguishing concreteness. Neurocomputing 429, 166\u2013173 (2021)","DOI":"10.1016\/j.neucom.2020.11.051"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"He, X., et al.: VIEMF: multimodal metaphor detection via visual information enhancement with multimodal fusion. Inf. Process. Manag. 61(3), 103652 (2024)","DOI":"10.1016\/j.ipm.2024.103652"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Xu, B., et al.: Met-meme: a multimodal meme dataset rich in metaphors. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval (2022)","DOI":"10.1145\/3477495.3532019"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, D., et al.: Towards multimodal metaphor understanding: a Chinese dataset and model for metaphor mapping identification. arXiv preprint arXiv:2501.02434 (2025)","DOI":"10.1145\/3773989"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"He, Kaiming, et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Cai, Y., Huiyu C., Wan, X.: Multi-modal sarcasm detection in twitter with hierarchical fusion model. In: Proceedings of the 57th annual meeting of the association for computational linguistics (2019)","DOI":"10.18653\/v1\/P19-1239"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Yang, B., et al.: Multimodal sentiment analysis with unidirectional modality translation. Neurocomputing 467, 130\u2013137 (2022)","DOI":"10.1016\/j.neucom.2021.09.041"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"de Toledo, Louren\u00e7o, G., Marcacini, R.M.: Transfer learning with joint fine-tuning for multimodal sentiment analysis. arXiv preprint arXiv:2210.05790 (2022)","DOI":"10.52591\/lxai202207173"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Sun, Licai, et al.: Efficient multimodal transformer with dual-level feature restoration for robust multimodal sentiment analysis. IEEE Trans. Affective Comput. 15(1), 309\u2013325 (2023)","DOI":"10.1109\/TAFFC.2023.3274829"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Xu, N., Zhixiong, Z., Mao. W.: Reasoning with multimodal sarcastic tweets via modeling cross-modality contrast and semantic association. In: Proceedings of the 58th annual meeting of the association for computational linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.349"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Lou, C., et al.: Affective dependency graph for sarcasm detection. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval (2021)","DOI":"10.1145\/3404835.3463061"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Liang, B., et al.: Multi-modal sarcasm detection via cross-modal graph convolutional network. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2022)","DOI":"10.18653\/v1\/2022.acl-long.124"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Yue, Tan, et al.: KnowleNet: knowledge fusion network for multimodal sarcasm detection. Inf. Fus. 100, 101921 (2023)","DOI":"10.1016\/j.inffus.2023.101921"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4445-5_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T03:44:23Z","timestamp":1768103063000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4445-5_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819544448","9789819544455"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4445-5_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"12 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Okinawa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"20 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2025.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}