{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T15:44:09Z","timestamp":1743090249519,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981779"},{"type":"electronic","value":"9789819981786"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"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\/978-981-99-8178-6_26","type":"book-chapter","created":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T10:02:54Z","timestamp":1701252174000},"page":"338-350","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Event Classification in\u00a0Social Media"],"prefix":"10.1007","author":[{"given":"Hexiang","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peifeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongqing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,30]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Liang, T., Lin, G., Wan, M., Li, T., Ma, G., Lv, F.: Expanding large pre-trained unimodal models with multimodal information injection for image-text multimodal classification. In: CVPR 2022, pp. 15471\u201315480 (2022)","DOI":"10.1109\/CVPR52688.2022.01505"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Abavisani, M., Wu, Li., Hu, S., Tetreault, J.R., Jaimes, A.: Multimodal categorization of crisis events in social media. In: CVPR 2020, pp. 14667\u201314677 (2020)","DOI":"10.1109\/CVPR42600.2020.01469"},{"key":"26_CR3","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: ICML 2021, pp. 8748\u20138763 (2021)"},{"key":"26_CR4","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NIPS 2017, pp. 5998\u20136008 (2017)"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Alam, F., Ofli, F., Imran, M.: CrisisMMD: multimodal twitter datasets from natural disasters. In: ICWSM 2018, pp. 465\u2013473 (2018)","DOI":"10.1609\/icwsm.v12i1.14983"},{"key":"26_CR6","unstructured":"Ofli, F., Alam, F., Imran, M.: Analysis of social media data using multimodal deep learning for disaster response. In: ISCRAM 2020, pp. 802\u2013811 (2020)"},{"key":"26_CR7","unstructured":"Kim, J., Jun, J., Zhang, B.: Bilinear attention networks. In: NeurIPS 2018, pp. 1571\u20131571 (2018)"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, T., et al.: Improving event extraction via multimodal integration. In: ACM Multimedia 2017, pp. 270\u2013278 (2017)","DOI":"10.1145\/3123266.3123294"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Li, M., Zareian, A., Zeng, Q., Whitehead S., Lu, D., Ji, H., Chang, S.: Cross-media structured common space for multimedia event extraction. In: ACL 2020, pp. 2557\u20132568 (2020)","DOI":"10.18653\/v1\/2020.acl-main.230"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Tong, M., Wang, S., Cao, Y., Xu, B., Li, J., Hou, L., Chua, T.: Image enhanced event detection in news articles. In: AAAI 2020, pp. 9040\u20139047 (2020)","DOI":"10.1609\/aaai.v34i05.6437"},{"key":"26_CR11","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT(1) 2019, pp. 4171\u20134186 (2019)"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Wadden, D., Wennberg, U., Luan, Y., Hajishirzi, H.: Entity, relation, and event extraction with contextualized span representations. In: EMNLP\/IJCNLP(1) 2019, pp. 5783\u20135788 (2019)","DOI":"10.18653\/v1\/D19-1585"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Lin, Y., Ji, H., Huang, F., Wu, L.: A joint neural model for information extraction with global features. In: ACL 2020, pp. 7999\u20138009 (2020)","DOI":"10.18653\/v1\/2020.acl-main.713"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Du, X., Cardie, C.: Event extraction by answering (almost) natural questions. In: EMNLP(1)2020, pp. 671\u2013683 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.49"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Lu, Y., et al.: Text2Event: controllable sequence-to-structure generation for end-to-end event extraction. In: ACL\/IJCNLP(1)2021, pp. 2795\u20132806 (2021)","DOI":"10.18653\/v1\/2021.acl-long.217"},{"key":"26_CR16","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1\u201367 (2020)"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Liu, X., Huang, H., Shi, G., Wang, B.: Dynamic prefix-tuning for generative template-based event extraction. In: ACL(1)2022, pp. 5216\u20135228 (2022)","DOI":"10.18653\/v1\/2022.acl-long.358"},{"key":"26_CR18","unstructured":"Kim, W., Son, B., Kim, I.: ViLT: vision-and-language transformer without convolution or region supervision. In: ICML 2021, pp. 5583\u20135594 (2021)"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Yang, Z., He, X., Gao, J., Deng, L., Smola, A.J.: Stacked attention networks for image question answering. In: CVPR 2016, pp. 21\u201329 (2016)","DOI":"10.1109\/CVPR.2016.10"},{"key":"26_CR20","unstructured":"Lu, J., Yang, J., Batra, D., Parikh, D.: Hierarchical question-image co-attention for visual question answering. In: NIPS 2016, pp. 289\u2013297 (2016)"},{"key":"26_CR21","unstructured":"Kiela, D., Bhooshan, S., Firooz, H., Testuggine, D.: Supervised Multimodal Bitransformers for Classifying Images and Text. In:arXiv preprint arXiv:1909.02950 (2019)"},{"key":"26_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR 2016, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"26_CR23","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR 2021 (2021)"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8178-6_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:31:08Z","timestamp":1709829068000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8178-6_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9789819981779","9789819981786"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8178-6_26","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,30]]},"assertion":[{"value":"30 November 2023","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":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}