A measure based on optimal matching in graph theory for document similarity

Authors:
Xiaojun Wan;Yuxin Peng
Affiliations:
Institute of Computer Science and Technology, Peking University, Beijing, China;Institute of Computer Science and Technology, Peking University, Beijing, China
Venue:
AIRS'04 Proceedings of the 2004 international conference on Asian Information Retrieval Technology
Year:
2004

Citing 9
Cited 0

Pictures of relevance: a geometric analysis of similarity measures

Journal of the American Society for Information Science
The smart document retrieval project

SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
Translating collocations for bilingual lexicons: a statistical approach

Computational Linguistics
Information Retrieval

Information Retrieval
Modern Information Retrieval

Modern Information Retrieval
An information-theoretic measure for document similarity

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Multi-paragraph segmentation of expository text

ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Document Similarity Using a Phrase Indexing Graph Model

Knowledge and Information Systems
Video clip retrieval by maximal matching and optimal matching in graph theory

ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

Measuring pairwise document similarity is critical to various text retrieval and mining tasks. The most popular measure for document similarity is the Cosine measure in Vector Space Model. In this paper, we propose a new similarity measure based on optimal matching in graph theory. The proposed measure takes into account the structural information of a document by considering the word distributions over different text segments. It first calculates the similarities for different pairs of text segments in the documents and then gets the total similarity between the documents optimally through optimal matching. We set up experiments of document similarity search to test the effectiveness of the proposed measure. The experimental results and user study demonstrate that the proposed measure outperforms the most popular Cosine measure.