Cross Language Information Retrieval using Selective Documents Technique and Query Expansion

Abstract

This paper is devoted to a new method that uses combined technique of pseudo relevance feedback and co-occurring term technique for query expansion to improve cross language information retrieval along with selective document technique to reduce search space. The base is an Information Retrieval (IR) system which uses cosine similarity technique to retrieve and rank documents and a multilingual module using dictionary based translation where the tf-idf values are calculated and stored in inverted index which is term based index. The threshold value is used as a selection medium for documents which reduces the search space of index reducing the overall time for retrieval. A Query Expansion (QE) module is added in to the said system. The aim is to use QE to overcome the limitations of dictionary based translation, and to retrieve more relevant results. The system is validated with several measures, which shows the difference in the result before expansion and after expansion.