A new query expansion approach using co-occurrence based semantic relationship of terms

In this thesis, we propose a new semantic-based query expansion approach, which uses co-occurrence information of words to acquire the semantics information of the terms. To do this, we first constructed word-context matrix using large English text corpus. Then we applied Singular Value Decomposition on the matrix to reduce the dimensionality. We validated the accuracy of semantic relationship on TOEFL synonym test. Our query expansion approach uses this semantic relationship to improve the existing query expansion methods available in the literature, namely Bose-Einstein and Kullback-Leibler. We evaluated our approach on Milliyet collection, which is a Turkish IR test bed containing more than 400K documents and 72 queries. The experimentation shows that our approach clearly improves the all exsiting QE methods in terms of major IR performance measures such as MAP, r-precision and recall.