Quality Improvement of Information Retrieval System Results by Document Classification

In traditional search engines, the most common way to show results for a query is to list documents in order of their computed relevance to the query. However, the ranking is independent of the topic of the document;so the results of different topics are not grouped together. In this situation, the user must scroll though many irrelevant results until his desired information need is found. One solution is to organize search results via classification. Many researchers have shown that classifying web pages can improve a search engineʹs ranking of results. Intuitively results should be more relevant when they match the class of a query. In this paper, we present a simple framework for classification-enhanced ranking that uses query class in combination with the classification of web pages to derive a class distribution for the query. In this regard, we propose a hybrid IR search strategy that begins with a 3-gram classification-based strategy and reverts to a ranked-list strategy if the user doesn’t find the target document in selected class.The experiment results on Hamshahri corpus show satisfactory results.