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Lingqi Kong and Shengquau Liu
With the development of the Internet, vast amounts of text information are being generated constantly. Methods for extracting the valuable parts from this information have become an important research field. Relation extraction aims to identify entities ...
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Weijun Li, Jintong Liu, Yuxiao Gao, Xinyong Zhang and Jianlai Gu
The task of named entity recognition (NER) is to identify entities in the text and predict their categories. In real-life scenarios, the context of the text is often complex, and there may exist nested entities within an entity. This kind of entity is ca...
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Weiwei Yuan, Wanxia Yang, Liang He, Tingwei Zhang, Yan Hao, Jing Lu and Wenbo Yan
The extraction of entities and relationships is a crucial task in the field of natural language processing (NLP). However, existing models for this task often rely heavily on a substantial amount of labeled data, which not only consumes time and labor bu...
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Marie-Therese Charlotte Evans, Majid Latifi, Mominul Ahsan and Julfikar Haider
Keyword extraction from Knowledge Bases underpins the definition of relevancy in Digital Library search systems. However, it is the pertinent task of Joint Relation Extraction, which populates the Knowledge Bases from which results are retrieved. Recent ...
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Qiuyue Li, Hao Sheng, Mingxue Sheng and Honglin Wan
Efficient document recognition and sharing remain challenges in the healthcare, insurance, and finance sectors. One solution to this problem has been the use of deep learning techniques to automatically extract structured information from paper documents...
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Dominik Warch, Patrick Stellbauer and Pascal Neis
In the digital transformation era, video media libraries? untapped potential is immense, restricted primarily by their non-machine-readable nature and basic search functionalities limited to standard metadata. This study presents a novel multimodal metho...
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Boris Stanoev, Goran Mitrov, Andrea Kulakov, Georgina Mirceva, Petre Lameski and Eftim Zdravevski
With the exponential growth of data, extracting actionable insights becomes resource-intensive. In many organizations, normalized relational databases store a significant portion of this data, where tables are interconnected through some relations. This ...
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Md Saiful Islam and Fei Liu
In the realm of artificial intelligence, knowledge graphs have become an effective area of research. Relationships between entities are depicted through a structural framework in knowledge graphs. In this paper, we propose to build a domain-specific medi...
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Qishun Mei and Xuhui Li
To address the limitations of existing methods of short-text entity disambiguation, specifically in terms of their insufficient feature extraction and reliance on massive training samples, we propose an entity disambiguation model called COLBERT, which f...
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Lianlian He, Hao Li and Rui Zhang
Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeferen...
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