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

Even though the ultimate aim of any information retrieval system is to fulfill 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 indeces. 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 resposnse time and precision of the retrieval.

کلیدواژه ها:

Information Retrieval ، Natural Language Processing ، Part of Speech ، Index storage reduction ، Term selection ، Stop-word detection