Earticle

초록

영어

Semantic similarity is fundamental operation in the field of computational lexical semantics, artificial intelligence and cognitive science. Accurate measurement of semantic similarity between words is crucial. The paper presents an effective algorithm for semantic similarity metric of word pairs. Different from previous work, in the new algorithm not only path length, but also IC values have been taken into account. We evaluate our model on the data set of Rubenstein and Goodenough, which is traditional and widely used. Coefficients of correlation between human ratings of similarity based on seven algorithms are calculated. Experiments show that the coefficient of our proposed algorithm with human judgment is 0.8820, which demonstrate that our new algorithm significantly outperformed others.

목차

Abstract
 1. Introduction
 2. Semantic Similarity Algorithm
  2.1. Path-based Similarity Algorithms
  2.2. Information Content based Similarity Algorithms
 3. A New Algorithm for Semantic Similarity Metric
 4. Evaluation
  4.1. Data Set
  4.2. Words Similarity Calculating Method
  4.3. Results Analysis
 5. Conclusion and Future Work
 References

키워드

저자

• Lingling Meng [ Computer Science and Technology Department, 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