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Extracting hyponymy of domain entity using Cascaded Conditional Random Fields

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Abstract

Entity hyponymy is an important semantic relation to build the domain ontology or knowledge graphs. Traditional extraction methods of domain concepts hyponymy are limited to manual annotation or specific patterns. Aiming at this problem, this paper proposed a new method of extracting hypernym–hyponym relations of domain entity with the CCRFs (Cascaded Conditional Random Fields), i.e., a two-layer CRFs model is employed to learn the hyponymy of domain entity concept. The lower-level of the CCRFs model is used to model the words by considering the dependence of long distance among words and identify the domain entity concept, which need to be combined in order. The pairs of entity concept can be obtained on the basis of the definition template characteristics. Then label the semantic pairs of concepts in high-level model by integrating assemblage characteristics and hyponymy demonstratives in feature template, finally identify the hypernym–hyponym relations between domain entities. Experiments on real-world data sets demonstrate the performance of the proposed algorithms.

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Authors and Affiliations

  1. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China

    Xiaojun Ma, Jianyi Guo, Zhengtao Yu, Cunli Mao, Yantuan Xian & Wei Chen

  2. Key Lab of Intelligent Information Processing, Kunming University of Science and Technology, Kunming, 650500, China

    Jianyi Guo, Zhengtao Yu, Cunli Mao, Yantuan Xian & Wei Chen

Authors
  1. Xiaojun Ma
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  2. Jianyi Guo
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  3. Zhengtao Yu
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  4. Cunli Mao
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  5. Yantuan Xian
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  6. Wei Chen
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Correspondence to Jianyi Guo.

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Xiaojun Ma. Born in 1991. Now studying in Kunming University of Science and Technology. Research interest including natural language process, information extraction.

Jianyi Guo. Born in 1964. Professor, ACM and CCF member. Graduated and received Master degree from Xi’an Jiaotong University in 1990. Since 1990, works at Kunming University of Science and Technology. Research interest including pattern recognition, natural language process, information extraction.

Zhengtao Yu. Born in 1970. Professor, ACM and CCF member. Graduated and received Ph.D. from school of computer science, Beijing Institute of Technology in 2005. Dean of school of information engineering and automation in Kunming University of Science and Technology. Research interest including pattern recognition, natural language process, information retrieval.

Cunli Mao. Born in 1977. Received Ph.D from the Kunming University of Science and Technology in 2013. Research interests include pattern recognition, natural language processing, information retrieva.

Yantuan Xian. Born in 1981. PhD candidate at Kunming University of Science and Technology, Kunming, China. Graduated and received his MS degree in Pattern Recognition and Intelligent System from Shenyang Institute of Automation (SIA) of Chinese Academy of Sciences in 2006. His major research interests are pattern recognition and information extraction.

Wei Chen. Born in 1983. Graduated and received his M.S. degree in computer software and theory from school of information of Yunnan University in 2009. His major research interests are information retrieval and information extraction.

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Ma, X., Guo, J., Yu, Z. et al. Extracting hyponymy of domain entity using Cascaded Conditional Random Fields. Pattern Recognit. Image Anal. 27, 637–644 (2017). https://doi.org/10.1134/S1054661817030208

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  • DOI: https://doi.org/10.1134/S1054661817030208

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