Earticle

초록

영어

Semantic similarity measure between concepts is a generic issue for many applications of computational linguistics and artificial intelligence. Information Content (IC) of concept is an important dimension of accessing semantic similarity between two concepts. Recently it has attracted great concern and become a hot topic. The paper gives a general overview of usage of information content in semantic similarity computing and then focuses on how to obtain information content. It reviews and analyses state-of-art IC models, including Corpus-dependent and Corpus-independent IC approach. Hyponym-based IC Model, leaves-based IC Model, concept’s topology structure based IC Model and relation-based IC Model are discussed respectively in detail. The important related issues are described. Finally further research is outlined for the improvement of IC.

목차

Abstract
 1. Introduction
 2. WordNet
 3. Corpus-dependent IC Model
 4. Corpus-independent IC Model
  4.1. Hyponym-based IC Model
  4.2. Concepts’ Topology-based IC Model
  4.3. Leaves-based IC Model
  4.4. Relation-based IC Model
 5. Discussion and Further Research
 6. Summary
 References

키워드

저자

• Lingling Meng [ Department of Educational Information Technology, East China Normal University, Shanghai, 200062, China ]
• Runqing Huang [ Shanghai Municipal People's Government, Shanghai, 200003, China ]
• Junzhong Gu [ Computer Science and Technology Department, East China Normal University, Shanghai, 200062, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605