On Computing Similarity Measures of Scientific Papers based on Vector Space Model and Probabilistic Models

초록

In this paper, we evaluate and compare the effectiveness and efficiency of the text-based similarity measures based on the vector space model and probabilistic model for scientific papers by using a real-world dataset. Our extensive experimental results show that the similarity measures based on the vector space model are more appropriate to compute the similarity of scientific papers.

키워드

probabilistic modelscientific paperstext- based similarityvector space model
제목
On Computing Similarity Measures of Scientific Papers based on Vector Space Model and Probabilistic Models
저자
Hamedani, Masoud ReyhaniLee, Sang-ChulKim, Sang Wook
발행일
2013-06
유형
Proceeding
저널명
2013 한국컴퓨터종합학술대회(KCC 2013)
페이지
300 ~ 302