N-gram and local context analysis for Persian text retrieval
AleAhmad, A, Hakimian, P, Mahdikhani, F and Oroumchian, F, N-gram and local context analysis for Persian text retrieval, 9th International Symposium on Signal Processing and Its Applications - ISSPA 2007, Sharjah, United Arab Emirates, 12-15 February 2007. Copyright Institute of Electrical and Electronics Engineers 2007. Original conference paper available here
The Persian language is one of the languages in Middle- East, so there are significant amount of Persian documents available on the Web. But there are relatively few studies on retrieval of Persian documents in the literature. In this experimental study, we assessed term and N-gram based vector space model and a query expansion method, namely, Local Context Analysis using different weighting schemes on a realistic corpus containing 160000+ news articles. Then we compared our results with previous works reported on Persian language. Our experimental results show that among the assessed methods, 4-gram based vector space model with Lnu.ltu weighting scheme has acceptable performance and Local Context Analysis has the best performance for Persian text retrieval so far.
A. AleAhmad, P. Hakimian, F. Mahdikhani, and Farhad Oroumchian. "N-gram and local context analysis for Persian text retrieval" University of Wollongong in Dubai - Papers.. Jan. 2007.
Available at: http://works.bepress.com/farhadoroumchian/7