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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2023,1,31]]},"abstract":"<jats:p>\n            Due to the lack of natural delimiters, most Chinese\n            <jats:bold>Named Entity Recognition (NER)<\/jats:bold>\n            approaches are character-based and utilize an external lexicon to leverage the word-level information. Although they have achieved promising results, the latent words they introduced are still non-contextualized. In this paper, we investigate three relations, i.e., adjacent relation between characters, character co-occurrence relation between latent words, and dependency relation among tokens, to address this issue. Specifically, we first establish the local context for latent words and then propose a masked self-attention mechanism to incorporate such local contextual information. Besides, since introducing external knowledge such as lexicon and dependency relation inevitably brings in some noises, we propose a gated information controller to handle this problem. Extensive experimental results show that the proposed approach surpasses most similar methods on public datasets and demonstrates its promising potential.\n          <\/jats:p>","DOI":"10.1145\/3531534","type":"journal-article","created":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T11:41:22Z","timestamp":1651232482000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Token Relation Aware Chinese Named Entity Recognition"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3631-3362","authenticated-orcid":false,"given":"Zeyu","family":"Huang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Software Development Environment, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4229-7215","authenticated-orcid":false,"given":"Wenge","family":"Rong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6908-042X","authenticated-orcid":false,"given":"Xiaofeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Sino-French Engineer School, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3329-3906","authenticated-orcid":false,"given":"Yuanxin","family":"Ouyang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3454-2468","authenticated-orcid":false,"given":"Chenghua","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Sheffield, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9421-1014","authenticated-orcid":false,"given":"Zhang","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Information Technology &amp; Management, University of International Business and Economics, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3130348.3130371"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/2684822.2685317"},{"issue":"2","key":"e_1_3_2_4_2","first-page":"101","article-title":"A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records","volume":"19","author":"Cai Xiaoling","year":"2019","unstructured":"Xiaoling Cai, Shoubin Dong, and Jinlong Hu. 2019. 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