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A continuous learning method for recognizing named entities by integrating domain contextual relevance measurement and Web farming mode of Web intelligence

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Abstract

Web farming can advance computational social science into a never-end learning process, in which social phenomena are dynamically and scientifically understood based on continuously produced, updated and expired data in the connected hyper world. Named entity recognition is a basic and core task of Web farming. However, the existing named entity recognition methods mainly depend on the complete, high-quality and well-labelled data sets and cannot meet the requirements of real-world applications. This paper proposes a continuous learning method for recognizing named entity by introducing the Web farming mode of Web Intelligence into the recognizing process. During the on-line stage, the domain contextual relevance of candidate entities is calculated by using the domain discrimination degree and the domain dependence function for recognizing the target entities. During the off-line stage, an active learning approach is designed to continuously improve the target corpus set by binding density-based clustering with semantic distance measurement. Experimental results show that the proposed method can effectively improve the accuracy of entity recognition and is more suitable for real-world applications.

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Acknowledgments

The work is supported by Science and Technology Project of Beijing Municipal Commission of Education (No. KM201710005026), National Basic Research Program of China (No.2014CB744600), Beijing Natural Science Foundation (No. 4182005), Beijing Key Laboratory of MRI and Brain Informatics.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Shaofu Lin, Jiangfan Gao, Shun Zhang, Xiaobo He, Ying Sheng & Jianhui Chen

  2. Beijing Institute of Smart City, Beijing University of Technology, Beijing, China

    Shaofu Lin & Jianhui Chen

  3. Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China

    Jianhui Chen

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  1. Shaofu Lin
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  2. Jiangfan Gao
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  3. Shun Zhang
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Correspondence to Jianhui Chen.

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This article belongs to the Topical Collection: Computational Social Science as the Ultimate Web Intelligence

Guest Editors: Xiaohui Tao, Juan D. Velasquez, Jiming Liu, and Ning Zhong

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Lin, S., Gao, J., Zhang, S. et al. A continuous learning method for recognizing named entities by integrating domain contextual relevance measurement and Web farming mode of Web intelligence. World Wide Web 23, 1769–1790 (2020). https://doi.org/10.1007/s11280-019-00758-x

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