A Relevance Feedback based Query Translation Enhancement Technique in Cross Language Information Retrieval

This paper proposes a new cross language relevance feedback technique called query translation enhancement (TE), and studies four algorithms for “estimating query terms’ translation probabilities based on obtained relevant document pair set”, which is an important step in query translation enhancement. We conduct a series of pseudo relevance feedback experiments to compare the four algorithms and validate the effectiveness of query translation enhancement. Experiment results show that all four algorithms can raise the effectiveness of retrieval results, but the algorithm based on word alignment technique has clear superiority. In addition, we find that query length and retrieval topics can greatly affect various query translation enhancement approaches.\u