Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Measuring Semantic Similarity between Words Using Wikipedia

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Lu Zhiqiang ; Sch. of Comput. Eng. & Sci., Shanghai Univ., Shanghai, China ; Shao Werimin ; Zhenhua, Y.

Semantic similarity measures play an important role in the extraction of semantic relations. Semantic similarity measures are widely used in natural language processing (NLP) and information retrieval (IR). This paper presents a new Web-based method for measuring the semantic similarity between words. Different from other methods which are based on taxonomy or search engine in Internet, our method uses snippets from Wikipedia1 to calculate the semantic similarity between words by using cosine similarity and TF-IDF. Also, the stemmer algorithm and stop words are used in preprocessing the snippets from Wikipedia. We set different threshold to evaluate our results in order to decrease the interference from noise and redundancy. Our method was empirically evaluated using Rubenstein-Good enough benchmark dataset. It gives higher correlation value (with 0.615) than some existing methods. Evaluation results show that our method improves ac-curacy and more robust for measuring semantic similarity between words.

Published in:

Web Information Systems and Mining, 2009. WISM 2009. International Conference on

Date of Conference:

7-8 Nov. 2009