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Data Analysis and Knowledge Discovery  0, Vol. Issue (): 1-    DOI: 10.11925/infotech.2020.0299
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Research on Automatic Identification of Hypernym-Hyponym Relations of Domain Concepts Based on Pattern and Projection Learning
Wang Sili,Zhu Zhongming,Yang Heng,Liu Wei
(Literature and Information Center of Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lan Zhou 730000, China)
(University of Chinese Academy of Sciences, Bei Jing, 100049, China)
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[Objective] Realize the automatic identification of the hypernym-hyponym relations of domain concepts to solve the problem of automatic acquisition and establishment of semantic relations between domain concepts in the automatic construction of domain ontology.

[Methods] The traditional unsupervised pattern-based method and the current advanced supervised-based projection learning methods are combined organically to apply to the automatic identification of domain concepts, and experimental research is carried out.

[Results] The method can identify the hypernym set of the domain concept. The identification accuracy in the medical field is 0.88, in the general field is 0.83, and in benchmark dataset BLESS is 0.85.

[Limitations] Affected by syntactic ambiguity, the quality of the corpus, the model accuracy has not yet reached its peak, and there are cases of misidentification.

[Conclusions] The model can find hypernym with different meanings of the same concept, and it also has a good identification effect on low-frequency words and named entities. In the future, consideration should be given to optimizing the identification method by appropriately reducing the weight of high-frequency top-level words and improving the quality of supervised corpus.

Key words Hearst pattern      Projection learning      Word embedding      Domain concept      Hypernym-hyponym relations      
Published: 03 August 2020
ZTFLH:  TP391.3,G250.7  

Cite this article:

Wang Sili, Zhu Zhongming, Yang Heng, Liu Wei. Research on Automatic Identification of Hypernym-Hyponym Relations of Domain Concepts Based on Pattern and Projection Learning . Data Analysis and Knowledge Discovery, 0, (): 1-.

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