N-gram and Local Context Analysis for Persian text retrieval

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...

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Bibliographic Details
Main Authors: Aleahmad, A., Hakimian, P., Mahdikhani, F., Oroumchian, F.
Format: Conference Proceeding
Language:English
Subjects:
Context modeling
Encoding
Extraterrestrial measurements
Functional analysis
Fuzzy systems
Information retrieval
Natural languages
Performance analysis
Testing
Writing
Online Access:Request full text
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Summary: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.
DOI:10.1109/ISSPA.2007.4555345