Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Ways of Extract Attribution
  2.2. Methods of Extract Attribute
 3. Entity Attribute Extraction Based on Deep Belief Network (EAEDB)
  3.1. Deep Belief Network
  3.2. Feature Extraction
  3.3. Process
 4. Experiment
  4.1. Data Set
  4.2. Result and Analysis
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Bei Zhong [ College of Information Engineering, Shanghai Maritime University, 201306 Shanghai, China ]
• Jin Liu [ College of Information Engineering, Shanghai Maritime University, 201306 Shanghai, China ]
• Yuanda Du [ College of Information Engineering, Shanghai Maritime University, 201306 Shanghai, China ]
• Yunlu Liaozheng [ College of Information Engineering, Shanghai Maritime University, 201306 Shanghai, China ]
• Jiachen Pu [ College of Information Engineering, Shanghai Maritime University, 201306 Shanghai, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605