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LSRGCN comprises two key modules: a shared segmentation-aware encoder and a multi-layer conditional random field decoder. The former part provides token representation including boundary information from sentence segmentation. The latter part can learn the connections between different entity classes and improve recognition accuracy through secondary decoding. We conduct experiments on four datasets. Experimental results demonstrate the effectiveness of our model. Additionally, extensive studies are conducted to enhance our understanding of the model and its capabilities.<\/jats:p>","DOI":"10.1007\/s40747-024-01551-8","type":"journal-article","created":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T08:02:30Z","timestamp":1723276950000},"page":"7893-7905","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Segmentation-aware relational graph convolutional network with multi-layer CRF for nested named entity recognition"],"prefix":"10.1007","volume":"10","author":[{"given":"Daojun","family":"Han","sequence":"first","affiliation":[]},{"given":"Zemin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yunsong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiangbo","family":"ma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4087-6798","authenticated-orcid":false,"given":"Juntao","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,10]]},"reference":[{"key":"1551_CR1","doi-asserted-by":"crossref","unstructured":"Sui D, Zeng X, Chen Y, Liu K, Zhao J (2023) Joint entity and relation extraction with set prediction networks. 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