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Process."],"published-print":{"date-parts":[[2023,7,31]]},"abstract":"<jats:p>Event extraction is an essential but challenging task in information extraction. This task has considerably benefited from pre-trained language models, such as BERT. However, when it comes to the trigger-word mismatch problem in languages without natural delimiters, existing methods ignore the complement of lexical information to BERT. In addition, the inherent multi-role noise problem could limit the performance of methods when one sentence contains multiple events. In this article, we propose a Mask-Attention-based BERT (MABERT) framework for Chinese event extraction to address the above problems. Firstly, in order to avoid trigger-word mismatch and integrate lexical features into BERT layers directly, a mask-attention-based transformer augmented with two mask matrices is devised to replace the original one in BERT. By the mask-attention-based transformer, the character sequence interacts with external lexical semantics sufficiently and keeps its structure information at the same time. Moreover, against the multi-role noise problem, we make use of event type information from representation and classification, two aspects to enrich entity features, where type markers and event-schema-based mask matrix are proposed. Experimental results on the widely used ACE2005 dataset show the effectiveness of our proposed MABERT on Chinese event extraction task compared with other state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3597455","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T09:48:55Z","timestamp":1684489735000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["MABERT: Mask-Attention-Based BERT for Chinese Event Extraction"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5055-8868","authenticated-orcid":false,"given":"Ling","family":"Ding","sequence":"first","affiliation":[{"name":"Tongji University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1091-1361","authenticated-orcid":false,"given":"Xiaojun","family":"Chen","sequence":"additional","affiliation":[{"name":"Tongji University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2842-7767","authenticated-orcid":false,"given":"Jian","family":"Wei","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9714-1210","authenticated-orcid":false,"given":"Yang","family":"Xiang","sequence":"additional","affiliation":[{"name":"Tongji University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,7,20]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/1629235.1629236"},{"key":"e_1_3_2_3_1","first-page":"529","volume-title":"Proceedings of COLING 2012","author":"Chen Chen","year":"2012","unstructured":"Chen Chen and Vincent Ng. 2012. 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