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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An Improved Approach to the Construction of Chinese Medical Knowledge Graph Based on CTD-BLSTM Model
IEEE Access, 2021 In the process of constructing the knowledge graph, entity recognition and relationship extraction are not only the most fundamental but also the most important tasks, and the effect of their model directly affects the final result of the graph.
Yang Wu, Xiyong Zhu, Yinan Zhu
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Chinese Named Entity Recognition Based on Word Fusion of Graph Attention Network [PDF]
Jisuanji gongcheng, 2022 Named entity recognition refers to the recognition of entities with specific meanings in texts.It is an important cornerstone of many downstream tasks in natural language processing.In the task of named entity recognition, the Collaborative Graph Network
SONG Xuhui, YU Hongtao, LI Shaomei
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Constructing marine expert management knowledge graph based on Trellisnet-CRF [PDF]
PeerJ Computer Science, 2022 Creating and maintaining a domain-specific database of research institutions, academic experts and scholarly literature is essential to expanding national marine science and technology.
Jiajing Wu +5 more
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Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance
AAAI Conference on Artificial Intelligence, 2021 Multi-modal named entity recognition (MNER) aims to discover named entities in free text and classify them into pre-defined types with images. However, dominant MNER models do not fully exploit fine-grained semantic correspondences between semantic units
Dong Zhang +5 more
semanticscholar +1 more source
Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning [PDF]
The Web Conference, 2022 Recently, graph collaborative filtering methods have been proposed as an effective recommendation approach, which can capture users’ preference over items by modeling the user-item interaction graphs.
Zihan Lin +3 more
semanticscholar +1 more source
Entity-level Interaction via Heterogeneous Graph for Multimodal Named Entity Recognition
Conference on Empirical Methods in Natural Language Processing, 2022 ,
Gang Zhao +5 more
semanticscholar +1 more source
Dictionary-based matching graph network for biomedical named entity recognition
Scientific Reports, 2023 Biomedical named entity recognition (BioNER) is an essential task in biomedical information analysis. Recently, deep neural approaches have become widely utilized for BioNER.
Yinxia Lou, Xun Zhu, Kai Tan
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DOZEN: Cross-Domain Zero Shot Named Entity Recognition with Knowledge Graph
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021 With the new developments of natural language processing, increasing attention has been given to the task of Named Entity Recognition (NER). However, the vast majority of work focus on a small number of large-scale annotated datasets with a limited ...
Hoang Van Nguyen +2 more
semanticscholar +1 more source