Mohammad-Ali Yaghoub-Zadeh-Fard - Iran University of Science and Technology, Tehran, Iran
Behrouz Minaei-Bidgoli - Iran University of Science and Technology, Tehran, Iran
Saeed Rahmani - Freelancer, Tehran, Iran
Saeed Shahrivari - Freelancer, Tehran, Iran

By the advent of new information resources, search engines have encountered a new challenge since they have been obliged to store a large amount of text materials. This is even more drastic for small-sized companies which are suffering from a lack of hardware resources and limited budgets. In such a circumstance, reducing index size is of paramount importance as it is to maintain the accuracy of retrieval. One of the primary ways to reduce the index size in text processing systems is to remove stop-words, frequently occurring terms which do not contribute to the information content of documents. Even though there are manually built stop-word lists almost for all languages in the world, stop-word lists are domain-specific; in other words, a term which is a stop-word in a specific domain may play an indispensable role in another one. This paper proposes an aggregated method for automatically building stop-word lists for Persian information retrieval systems. Using part of speech tagging and analyzing statistical features of terms, the proposed method tries to enhance the accuracy of retrieval and minimize potential side effects of removing informative terms. The experiment results show that the proposed approach enhances the average precision, decreases the index storage size, and improves the overall response time.

Information Retrieval, Natural Language Processing, Part of Speech Tagging, Index Size Reduction, Stop-words