Earticle

초록

영어

Short text similarity measure is the basis of classification and duplicate checking of the short texts. Allowing for the insufficient consideration of the sentence semantic and structure information in similarity calculation between two short texts, we propose a novel method of short text similarity calculation based on double vector space model on the basis of traditional vector space model. Creatively transforming traditional vector space model into double vector space model. We utilize the numeral data link relations of Wikipedia to calculate semantic similarity between words, and calculate text structure similarity by dependency trees. Finally, we get the synthetic similarity by combining the semantic similar vector and structure similar vector. Our experiment results demonstrate that the proposed method has higher accuracy than other methods.

목차

Abstract
 1. Introduction
 2. DVSM-WDT Model
 3. Short Text Similarity Measure Based on Double Vector Space Model
  3.1. The Calculation Method of the Semantic Similarity
  3.2. The Calculation Method of the Structure Similarity Based on Semantic Dependency Trees
  3.3. The Calculation Method of Short Text Similarity
 4. Experiment
  4.1. The Source of Data and the Datasets
  4.2. The Evaluation Method of Algorithm
  4.3. The Analysis of Experiment Results
 5. Conclusion
 References

키워드

저자

• Ying Liu [ School of Information Science and Technology, Beijing Forestry University, Beijing, China ]
• Dongmei Li [ School of Information Science and Technology, Beijing Forestry University, Beijing, China ]
• Cong Dai [ School of Information Science and Technology, Beijing Forestry University, Beijing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605