{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:12:29Z","timestamp":1777129949063,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>Sequence labeling serves as the most commonly used scheme for Chinese named entity recognition(NER). However, traditional sequence labeling methods classify tokens within an entity into different classes according to their positions. As a result, different tokens in the same entity may be learned with representations that are isolated and unrelated in target representation space, which could finally negatively affect the subsequent performance of token classification. In this paper, we point out and define this problem as Entity Representation Segmentation in Label-semantics. And then we present NerCo: Named entity recognition with Contrastive learning, a novel NER framework which can better exploit labeled data and avoid the above problem. Following the pretrain-finetune paradigm, NerCo firstly guides the encoder to learn powerful label-semantics based representations by gathering the encoded token representations of the same Semantic Class while pushing apart that of different. Subsequently, NerCo finetunes the learned encoder for final entity prediction. Extensive experiments on several datasets demonstrate that our framework can consistently improve the baseline and achieve state-of-the-art performance.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/587","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"5287-5295","source":"Crossref","is-referenced-by-count":9,"title":["NerCo: A Contrastive Learning Based Two-Stage Chinese NER Method"],"prefix":"10.24963","author":[{"given":"Zai","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University, China"},{"name":"Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Xi\u2019an Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University, China"},{"name":"Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Xi\u2019an Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haokun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University, China"},{"name":"Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Xi\u2019an Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huang","family":"Xu","sequence":"additional","affiliation":[{"name":"Servyou Software Group Co., Ltd"},{"name":"Hangzhou City University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaodong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Xi\u2019an Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuefei","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University, China"},{"name":"Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Xi\u2019an Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Dong","sequence":"additional","affiliation":[{"name":"Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Xi\u2019an Jiaotong University, China"},{"name":"School of Distance Education, Xi\u2019an Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghua","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University, China"},{"name":"Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Xi\u2019an Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","theme":"Artificial Intelligence","location":"Macau, SAR China","acronym":"IJCAI-2023","number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2023,8,19]]},"end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:52:03Z","timestamp":1691743923000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/587"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/587","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}