Earticle

초록

영어

The traditional information extraction methods based on specific domain usually depend on the domain dictionaries to discover the text feature. It is inconvenient for reproducing and difficult to transplant in multi-domain environment. The application scope is limited seriously. Oriented to the deficiencies above, a multi-domain web text feature extraction model for e-Science is proposed (named e-FTM). This model adopts the Chinese split words technology without dictionary into the process of multi-domain text feature discovery and avoids the dependency of domain dictionaries effectively. With the help of classification of common and individual features, the model tracks the generation and the development trend of domain events dynamically, and forms a couple of local data centers eventually. Through cooperative scheduling the domain knowledge between different local data centers, the knowledge utilization efficiency of the domain information in the global scope is improved sharply. To validate the performance, the experiments on the multi-domain text feature extraction, topic features dynamical tracking and the domain knowledge cooperative scheduling demonstrate that the model has higher application validity and practicality in e-Science environment.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Problem Description and General Process
 4. Multi-Domain Web Text Feature Extraction Model for e-Science Environment
  4.1. Multi-Domain Web Text Feature Discovery
  4.2. The Topic Feature Tracking
 5. Experimental Verification
  5.1. Experiment Design
  5.2. Experiment Results
 6. Conclusions and Future Work
 References

키워드

저자

• Weng Yu [ School of Information Engineering, Minzu University of China, Beijing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605