A Noval Approach to Measure the Semantic Similarity for Information Retrieval

In this fast paced multitasking world, internet users are increasing day by day so is our database is increasing and manually maintaining similarity between words of database is a troublesome task. Maintaining semantic similitude between words is substantial chore in chromatic areas such as Natural Language processing tasks like Word sense disambiguation, query expansion as well as web chore such as document bunching, community excavating and automatic metadata breeding. With its wide area applications and usage, in a document still it is very tough to calculate the measure for any two words or entities. We propound a formula using Google, computing semantic similarity employing page count (retrieved by Google only) as a metric. The bounced method outperforms or contribute almost same results to chromatic base lines and compared with the previously proposed web-based semantic similarity methods. The results obtained compared with various online tools like UMBC, SEMILAR etc. Moreover, proposed method has less computation complexity as well as significantly improves the exactitude and efficiency of calculating semantic similarity between two words.