{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:37:29Z","timestamp":1777696649760,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2024,7,17]]},"abstract":"<jats:p>Named Entity Recognition (NER) is a fundamental task that aids in the completion of other tasks such as text understanding, information retrieval and question answering in Natural Language Processing (NLP). In recent years, the use of a mix of character-word structure and dictionary information for Chinese NER has been demonstrated to be effective. As a representative of hybrid models, Lattice-LSTM has obtained better benchmarking results in several publicly available Chinese NER datasets. However, Lattice-LSTM does not address the issue of long-distance entities or the detection of several entities with the same character. At the same time, the ambiguity of entity boundary information also leads to a decrease in the accuracy of embedding NER. This paper proposes ELCA: Enhanced Boundary Location for Chinese Named Entity Recognition Via Contextual Association, a method that solves the problem of long-distance dependent entities by using sentence-level position information. At the same time, it uses adaptive word convolution to overcome the problem of several entities sharing the same character. ELCA achieves the state-of-the-art outcomes in Chinese Word Segmentation and Chinese NER.<\/jats:p>","DOI":"10.3233\/ida-230383","type":"journal-article","created":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T11:47:19Z","timestamp":1721389639000},"page":"973-990","source":"Crossref","is-referenced-by-count":2,"title":["ELCA: Enhanced boundary location for Chinese named entity recognition via contextual association"],"prefix":"10.1177","volume":"28","author":[{"given":"Yizhao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, South China Normal University, Guangzhou, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shun","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Computer Science, South China Normal University, Guangzhou, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuncheng","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science, South China Normal University, Guangzhou, Guangdong, China"},{"name":"School of Artificial Intelligence, South China Normal University, Foshan, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-230383_ref1","first-page":"1137","article-title":"A neural probabilistic language model","volume":"3","author":"Bengio","year":"2003","journal-title":"The Journal of Machine Learning Research"},{"key":"10.3233\/IDA-230383_ref5","first-page":"1","article-title":"A new chinese text clustering algorithm based on wrd and improved k-means","author":"Cui","year":"2023","journal-title":"Intelligent Data Analysis"},{"key":"10.3233\/IDA-230383_ref8","doi-asserted-by":"crossref","unstructured":"T. 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