Journal
INFORMATION SCIENCES
Volume 180, Issue 20, Pages 4031-4041Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2010.06.021
Keywords
Text similarity; Short text similarity; Information retrieval; Query expansion; Text mining; Question answering
Categories
Funding
- City University of Hong Kong [7002488]
- National Grand Fundamental Research 973 Program of China [2003CB317002]
Ask authors/readers for more resources
A novel modeling method for a collection of short text snippets is presented in this paper to measure the similarity between pairs of snippets. The method takes account of both the semantic and statistical information within the short text snippets, and consists of three steps. Given a set of raw short text snippets, it first establishes the initial similarity between words by using a lexical database. The method then iteratively calculates both word similarity and short text similarity. Finally, a proximity matrix is constructed based on word similarity and used to convert the raw text snippets into vectors. Word similarity and text clustering experiments show that the proposed short text modeling method improves the performance of existing text-related information retrieval (IR) techniques. (C) 2010 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available