Tuning Local Context Analysis for Farsi Documents

Farsi language is one of the dominant languages in middle-east. A lot of work has been done on Farsi retrieval systems. Local context analysis is a query expansion method to improve retrieval performance. In this paper we have tried to tune LCA for Farsi language. We used Hamshahri collection and 60 queries to tune three parameters in LCA method which are number of concepts used for query expansion, number of initially retrieved documents for local feedback and number of passages for concept discovery and weighting. The results reveal that there is a possible optimization point when 20 concepts are used; however, increasing the other two parameters which are number retrieved documents and number of passages used for local feedback almost always yields better results.