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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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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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Yue Zha, Yuanzhi Ke, Xiao Hu and Caiquan Xiong
Named entity recognition (NER) is particularly challenging for medical texts due to the high domain specificity, abundance of technical terms, and sparsity of data in this field. In this work, we propose a novel attention layer, called the ?ontology atte...
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Xiaolong Li, Wu Zhang, Yanjie Wang, Yongbin Tan and Jing Xia
As an important carrier of individual information, the resume is an important data source for studying the spatio-temporal evolutionary characteristics of individual and group behaviors. This study focuses on spatio-temporal information extraction and ge...
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Wei Zhang, Jingtao Meng, Jianhua Wan, Chengkun Zhang, Jiajun Zhang, Yuanyuan Wang, Liuchang Xu and Fei Li
Social media is widely used to share real-time information and report accidents during natural disasters. Named entity recognition (NER) is a fundamental task of geospatial information applications that aims to extract location names from natural languag...
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Qiang He, Guowei Chen, Wenchao Song and Pengzhou Zhang
Named entity recognition (NER) is a subfield of natural language processing (NLP) that identifies and classifies entities from plain text, such as people, organizations, locations, and other types. NER is a fundamental task in information extraction, inf...
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Shuiyan Li, Rongzhi Qi and Shengnan Zhang
Compared with English named entity recognition (NER), Chinese NER faces significant challenges due to the flexible, non-standard word formation and vague word boundaries, which cause a lot of boundary ambiguity and reduce the accuracy of entity identific...
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Lin He, Shengnan Wang and Xinran Cao
Shipping Enterprise Credit Named Entity Recognition (NER) aims to recognize shipping enterprise credit entities from unstructured shipping enterprise credit texts. Aiming at the problem of low entity recognition rate caused by complex and diverse entitie...
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Yeon-Ji Park, Min-a Lee, Geun-Je Yang, Soo Jun Park and Chae-Bong Sohn
The BioBERT Named Entity Recognition (NER) model is a high-performance model designed to identify both known and unknown entities. It surpasses previous NER models utilized by text-mining tools, such as tmTool and ezTag, in effectively discovering novel ...
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Feiyang Ye, Liang Huang, Senjie Liang and KaiKai Chi
Named entity recognition (NER) in a few-shot setting is an extremely challenging task, and most existing methods fail to account for the gap between NER tasks and pre-trained language models. Although prompt learning has been successfully applied in few-...
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