Earticle

초록

영어

Semantic similarity between word senses is hot topic in many applications of computational linguistics and artificial intelligence, such as word sense disambiguation, information extraction, semantic annotation and ontology learning. Many methods for calculating word sense similarity have been proposed. In recent years the methods based on WordNet have shown its talents and attracted great concern. In the paper, we present a new method in WordNet for calculating word sense similarity, which is noun and is-a relation based. We evaluate our method on the data set of Rubenstein and Goodenough, which is traditional and widely used. The correlation with human judgment is o.8804 in proposed measure, which is more close to human judgments than related works. Experiments show that our new measure significantly outperformed than other existing computational methods.

목차

Abstract
 1. Introduction
 2. Semantic Similarity Measures
  2.1. WordNet
  2.2. Path-based Measures
  2.3. Information Content based Measures
 3. A New Method for Calculating Word Sense Similarity
 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 ]
• Junzhong Gu [ Computer Science and Technology Department East China Normal University ]

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