Earticle

초록

영어

This paper studies the problem of extracting Chinese comparative sentences from user reviews, which is a problem of text classification in the level of sentence. This paper first deals with the class skewed problem of review data, and then builds a SVM (support vector machine) model to classify comparative and non-comparative sentences into different groups on a balanced dataset. Various linguistic and statistical features are introduced to characterize a sentence. Experiments were conducted on user-generated product reviews. As a result, our experiments show significant performance, an overall F-score of 85.87%.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Balancing of Corpus
  3.1. Sentence Types
  3.2. Data Balance Strategy
 4. Extracting Comparative Sentences in Balanced Corpus
  4.1. Mining Sequence Pattern Features
  4.2. Manual Rule Features
 5. Experimental Results
  5.1. Data Sets
  5.2. Experimental Results
 6. Conclusion
 Acknowledgement
 References

키워드

저자

• Wei Wang [ School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China ]
• TieJun Zhao [ School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China ]
• GuoDong Xin [ School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China ]
• YongDong Xu [ School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605