Measuring Semantic Similarity between Words Using Wikipedia

被引：11

作者：

Lu Zhiqiang ^{[1
]}

Shao Werimin ^{[1
]}

Yu Zhenhua ^{[2
]}

机构：

[1] Shanghai Univ, Sch Engn & Comp Sci, Shanghai, Peoples R China

[2] Second Ltd Liabil Co, Shandong, Peoples R China

来源：

WISM: 2009 INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND MINING, PROCEEDINGS | 2009年

关键词：

Text semantic similarity; wikipedia; TF-IDF; cosine similarity;

D O I：

10.1109/WISM.2009.59

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 Wikipedia(1) 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-Goodenough benchmark dataset. It gives higher correlation value (with 0.615) than some existing methods. Evaluation results show that our method improves accuracy and more robust for measuring semantic similarity between words.

引用

页码：251 / +

页数：3

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