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In recent neural network developments, deep structures that map categorized features into continuous representations have been adopted. Using this approach, a dense space saturated with high-order abstract semantic information is unfolded, and the prediction is based on distributed feature representations. In this paper, the positions of NEs in a sentence are represented as continuous values. Then, a regression operation is introduced to regress the boundaries of NEs in a sentence. Based on boundary regression, we design a boundary regression model to support nested NE recognition. It is a multiobjective learning framework that simultaneously predicts the classification score of an NE candidate and refines its spatial location in a sentence. This model was evaluated on the ACE 2005 Chinese and English corpus and the GENIA corpus. State-of-the-art performance was experimentally demonstrated for nested NE recognition, which outperforms related works about 5% and 2% respectively. Our model has the advantage to resolve nested NEs and support boundary regression for locating NEs in a sentence. By sharing parameters for predicting and locating, this model enables more potent nonlinear function approximators to enhance model discriminability.<\/jats:p>","DOI":"10.1007\/s12559-022-10058-8","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T04:03:56Z","timestamp":1663905836000},"page":"534-551","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Boundary Regression Model for Nested Named Entity Recognition"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9946-3157","authenticated-orcid":false,"given":"Yanping","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lefei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghua","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruizhang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liyuan","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhui","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongbin","family":"Qing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,23]]},"reference":[{"key":"10058_CR1","doi-asserted-by":"crossref","unstructured":"McCallum\u00a0A, Li W. 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