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Extracting Attributes of Named Entity from Unstructured Text with Deep Belief Network
Bei Zhong,Jin Liu,Yuanda Du,Yunlu Liaozheng,Jiachen Pu 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.5
Entity attribute extraction is a challenging research topic with broad application prospects. Many researchers had proposed rule based or statistic based approaches to deal with the extraction task in a variety of application areas. Recently, deep learning had shown its capacity to model high-level abstractions in data by using multiple processing layers network with complex structures. However there has no research reported to conduct entity attribute extraction with deep learning method. In this paper, we propose a new approach to extract the entities’ attributes from unstructured text corpus that was gathered from Web. The proposed method is an unsupervised machine learning method that extracts the entity attributes utilizing deep belief network (DBN). Experiment results show that, with our method, entity attributes can be extracted accurately and manual intervention can be reduced when compared with tradition methods.
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