Measuring semantic similarity between words using web search engines

Semantic similarity measures play important roles in information retrieval and Natural Language Processing. Previous work in semantic web-related applications such as community mining, relation extraction, automatic meta data extraction have used various semantic similarity measures. Despite the usefulness of semantic similarity measures in these applications, robustly measuring semantic similarity between two words (or entities) remains a challenging task. We propose a robust semantic similarity measure that uses the information available on the Web to measure similarity between words or entities. The proposed method exploits page counts and text snippets returned by a Web search engine. We define various similarity scores for two given words P and Q, using the page counts for the queries P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using automatically extracted lexico-syntactic patterns from text snippets. These different similarity scores a...

• Type: Conference
• Year: 2007
• Where: WWW
• Authors: Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka
• Keywords: Internet Technology, Robust Semantic Similarity, Semantic Similarity Measure, Semantic Similarity Measures, WWW 2007