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A comprehensive review of conditional random fields: variants, hybrids and applications

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Abstract

The conditional random fields (CRFs) model plays an important role in the machine learning field. Driven by the development of the artificial intelligence, the CRF models have enjoyed great advancement. To analyze the recent development of the CRFs, this paper presents a comprehensive review of different versions of the CRF models and their applications. On the basis of elaborating on the background and definition of the CRFs, it analyzes three basic problems faced by the CRF models and reviews their latest improvements. Based on that, it presents the applications of the CRFs in the natural language processing, computer vision, biomedicine, Internet intelligence and other relevant fields. At last, specific analysis and future directions of the CRFs are discussed.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 71671057).

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  1. School of Management, Hefei University of Technology, Hefei, Anhui, China

    Bengong Yu & Zhaodi Fan

  2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, Anhui, China

    Bengong Yu

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Yu, B., Fan, Z. A comprehensive review of conditional random fields: variants, hybrids and applications. Artif Intell Rev 53, 4289–4333 (2020). https://doi.org/10.1007/s10462-019-09793-6

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