Earticle

초록

영어

Measuring semantic similarity of word pairs is a popular topic for many years. It is crucial in many applications, such as information extraction, semantic annotation, question answering system and so on. It is mandatory to design accurate metric for improving the performance of the bulk of applications relying on it. The paper presents a new metric for measuring word sense similarity using path and information content. Different from previous works, the new metric not only reflects the semantic density information, but also reflects the path information. It is evaluated on the dataset provided by Rubenstein and Goodenough. Experiments demonstrate that the coefficient based on our proposed metric with human judgment is 0.8817, which is significantly outperformed than other existing methods.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. WordNet
  2.2. Definitions
  2.3. Semantic Similarity Metrics
 3. A New Semantic Similarity Metric Based on WordNet
 4. Evaluation
  4.1. Data set and Words Similarity Calculating Method
  4.2. Results Analysis
 5. Conclusion and Future Work
 Reference

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 Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605