An Efficient Set of Parts of Speech in Persian Information Retrieval Systems

Abstract

Even though the ultimate aim of any information retrieval system is to fulfil its users’ expectations, reducing index storage size and enhancing the system performance are sometimes infinitely preferable, especially for small-sized companies suffering from a lack of hardware resources. For such companies, it is of paramount importance to remove noninfomative terms from their indices. Selecting a proper set of terms makes it possible to reduce the index storage size and consequently enhance the retrieval performance. In this paper, using parts of speech tagging, we show how to reduce the index storage size without losing precision. Through an experimental process and using Hamshahri corpora, we identify the most effective parts of speech in Persian language. Results demonstrate improvements in the response time and precision of the retrieval