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Inf. Syst."],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>Event extraction is a long-standing and challenging task in natural language processing, and existing studies mainly focus on extracting events within sentences. However, a significant problem that has not been carefully investigated is whether an \u201cevent topic\u201d can be identified to represent the main aspects of extracted events. This article formulates the \u201ctopic event\u201d extraction problem, aiming to identify a representative event from extracted ones. Specifically, after defining the topic event, we develop a multifocal graph-based framework to handle the extraction task. To enrich the associations of events and their tokens, we construct four event graphs, including the event subgraph and three event-associated graphs (i.e., event dependency parsing graph, event organization graph, and event share token graph), that reflect the internal and external structures of events, respectively. Subsequently, we design a multi-attention event-graph neural network to capture these event graph structures and improve event subgraph embedding. Finally, the output embeddings in the last layer of each channel are concatenated and fed into a fully connected network for topic event recognition. Extensive experiments validate the effectiveness of our method, and the results confirm its superiority over state-of-the-art baselines. In-depth analyses explore the essential factors (e.g., graph structures, attentions, feature generation method, etc.) determining the extraction performance.<\/jats:p>","DOI":"10.1145\/3696353","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T15:55:08Z","timestamp":1726761308000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A Multifocal Graph-Based Neural Network Scheme for Topic Event Extraction"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8835-5134","authenticated-orcid":false,"given":"Qizhi","family":"Wan","sequence":"first","affiliation":[{"name":"Jiangxi University of Finance and Economics, Nanchang, China and Jiangxi Key Laboratory of Data and Knowledge Engineering, Nanchang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6222-1015","authenticated-orcid":false,"given":"Changxuan","family":"Wan","sequence":"additional","affiliation":[{"name":"Jiangxi University of Finance and Economics, Nanchang, China and Jiangxi Key Laboratory of Data and Knowledge Engineering, Nanchang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6494-1174","authenticated-orcid":false,"given":"Keli","family":"Xiao","sequence":"additional","affiliation":[{"name":"Stony Brook University, Stony Brook, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9940-1351","authenticated-orcid":false,"given":"Rong","family":"Hu","sequence":"additional","affiliation":[{"name":"Jiangxi University of Finance and Economics, Nanchang, China and Jiangxi Key Laboratory of Data and Knowledge Engineering, Nanchang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1093-2744","authenticated-orcid":false,"given":"Dexi","family":"Liu","sequence":"additional","affiliation":[{"name":"Jiangxi University of Finance and Economics, Nanchang, China and Jiangxi Key Laboratory of Data and Knowledge Engineering, Nanchang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0629-165X","authenticated-orcid":false,"given":"Guoqiong","family":"Liao","sequence":"additional","affiliation":[{"name":"Jiangxi University of Finance and Economics, Nanchang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0230-8004","authenticated-orcid":false,"given":"Xiping","family":"Liu","sequence":"additional","affiliation":[{"name":"Jiangxi University of Finance and Economics, Nanchang, China and Jiangxi Key Laboratory of Data and Knowledge Engineering, Nanchang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4549-7865","authenticated-orcid":false,"given":"Yuxin","family":"Shuai","sequence":"additional","affiliation":[{"name":"Jiangxi University of Finance and Economics, Nanchang, China and Jiangxi Key Laboratory of Data and Knowledge Engineering, Nanchang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,30]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1207","volume-title":"Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI)","author":"Ahmad Wasi Uddin","year":"2021","unstructured":"Wasi Uddin Ahmad, Nanyun Peng, and Kai-Wei Chang. 2021. 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