A Graph Attention Model for Dictionary-Guided Named Entity Recognition [PDF]
IEEE Access, 2020 The lack of human annotations has been one of the main obstacles for neural named entity recognition in low-resource domains. To address this problem, there have been many efforts on automatically generating silver annotations according to domain ...
Yinxia Lou +3 more
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BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework [PDF]
BMC Bioinformatics, 2022 Background Automatic and accurate recognition of various biomedical named entities from literature is an important task of biomedical text mining, which is the foundation of extracting biomedical knowledge from unstructured texts into structured formats.
Xiangwen Zheng +5 more
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A lattice-transformer-graph deep learning model for Chinese named entity recognition [PDF]
Journal of Intelligent Systems, 2023 Named entity recognition (NER) is the localization and classification of entities with specific meanings in text data, usually used for applications such as relation extraction, question answering, etc.
Lin Min +4 more
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Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards [PDF]
ISPRS International Journal of Geo-Information, 2019 Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance.
Runyu Fan +5 more
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Bipartite Flat-Graph Network for Nested Named Entity Recognition [PDF]
, 2020 In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner ...
Luo, Ying, Zhao, Hai
core +4 more sources
What can knowledge graph do for few-shot named entity recognition
Web SemanticsDue to its extensive applicability in various downstream domains, few-shot named entity recognition (NER) has attracted increasing attention, particularly in areas where acquiring sufficient labeled data poses a significant challenge. Recent studies have
Binling Nie, Yiming Shao, Yigang Wang
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Few-Shot Named Entity Recognition Based on the Collaborative Graph Attention Network
IEEE AccessFew-shot Named Entity Recognition (NER) aims to extract entity information from limited annotated samples, addressing the scarcity of data in specialized domains.
Haoran Niu, Zhaoman Zhong
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Named Entity Recognition in Tourism Based on Directed Graph Model [PDF]
Jisuanji gongcheng, 2022 Named entity recognition in the field of tourism is an important part in the construction of tourism knowledge graph.Compared with entities in the general field, entities in the tourism field are characterized by the long name, polysemy and frequent ...
CUI Liping, Altenbek Gulila, WANG Zhiyue
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Adaptive Graph Representation for Clustering
IEEE Access, 2022 Many graph construction methods for clustering cannot consider both local and global data structures in the construction of initial graph. Meanwhile, redundant features or even outliers and data with important characteristics are addressed equally in the
Mei Chen +5 more
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News Image-Text Matching With News Knowledge Graph
IEEE Access, 2021 Image-text matching using the image caption method has made a great progress. However, there are many named entities in news text, and existing approaches are unable to directly generate named entities in the news image caption.
Zhao Yumeng +3 more
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