{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T17:37:19Z","timestamp":1769708239593,"version":"3.49.0"},"reference-count":3,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,10,4]]},"abstract":"<jats:p>In the entity extraction task, there are some complex extraction problems, such as nested entity, entity boundary recognition, context ambiguity, and multi-instance entity recognition. Entity nesting is an important challenge in relational extraction. The main reason of entity nesting problem is that the boundary information between entities is not clear. In order to solve the entity nesting problem at the fragment level, while preserving the relationship between fragments with the same characteristics and improving efficiency, we proposed a brand new fragment annotation method. On the basis of traditional fragment annotation method, combined with pointer annotation method, we designed an annotation method of \"ergodic enumeration + group mapping\". On the basis of this method, an entity extraction model is designed: Span-Extraction Based Entity Extraction Model (LMA). Our model underwent a series of validations in the English data sets New York Times(NYT) and WEBNLG, showing significant improvements over the baseline model F1. It can effectively alleviate the above problems.<\/jats:p>","DOI":"10.3233\/jifs-231766","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T10:15:25Z","timestamp":1689070525000},"page":"5647-5657","source":"Crossref","is-referenced-by-count":0,"title":["A multiple head selection joint entity-relation extraction model"],"prefix":"10.1177","volume":"45","author":[{"given":"Jiafeng","family":"Suo","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Changchun University of Technology, Changchun, China"}]},{"given":"Dongchen","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Changchun University of Technology, Changchun, China"}]},{"given":"Hui","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Changchun University of Technology, Changchun, China"}]}],"member":"179","reference":[{"issue":"S02","key":"10.3233\/JIFS-231766_ref5","first-page":"76","article-title":"A dongbieke: Extraction of lstm relationships based on neuronal block level attention mechanism","author":"Wutianhao","year":"2020","journal-title":"Computer Application Research"},{"issue":"10","key":"10.3233\/JIFS-231766_ref6","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"Lstm: A search space odyssey","volume":"28","author":"Greff","year":"2016","journal-title":"IEEE Transactions on Neural Networks Learning Systems"},{"issue":"1","key":"10.3233\/JIFS-231766_ref24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2668133","article-title":"A fast parallel stochastic gradient method for matrix factorization in shared memory systems","volume":"6","author":"Chin","year":"2015","journal-title":"Acm Transactions on Intelligent Systems and Technology"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-231766","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T06:53:41Z","timestamp":1769669621000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-231766"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,4]]},"references-count":3,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3233\/jifs-231766","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,4]]}}}