Bipartite Flat-Graph Network for Nested Named Entity Recognition [PDF]
Annual Meeting of the Association for Computational Linguistics, 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 +6 more sources
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
doaj +3 more sources
Cross-domain Named Entity Recognition via Graph Matching [PDF]
Findings, 2022 Cross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario. A common practice is first to learn a NER model in a rich-resource general domain and then adapt the model to specific domains.
Junhao Zheng, Haibin Chen, Qianli Ma
openalex +3 more sources
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
doaj +2 more sources
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
doaj +2 more sources
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
doaj +2 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
doaj +2 more sources
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
doaj +2 more sources
Semantic-enhanced graph neural network for named entity recognition in ancient Chinese books [PDF]
Scientific ReportsNamed entity recognition (NER) plays a crucial role in the extraction and utilization of knowledge of ancient Chinese books. However, the challenges of ancient Chinese NER not only originate from linguistic features such as the use of single characters ...
Yongrui Xu +5 more
doaj +2 more sources
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
doaj +1 more source