Estimation of statistical translation models based on mutual information for ad hoc information retrieval

As a principled approach to capturing semantic relations of words in information retrieval, statistical translation models have been shown to outperform simple document language models which rely on exact matching of words in the query and documents. A main challenge in applying translation models to ad hoc information retrieval is to estimate a translation model without training data. Existing work has relied on training on synthetic queries generated based on a document collection. However, this method is computationally expensive and does not have a good coverage of query words. In this paper, we propose an alternative way to estimate a translation model based on normalized mutual information between words, which is less computationally expensive and has better coverage of query words than the synthetic query method of estimation. We also propose to regularize estimated translation probabilities to ensure sufficient probability mass for self-translation. Experiment results show tha...

• Type: Conference
• Year: 2010
• Where: SIGIR
• Authors: Maryam Karimzadehgan, ChengXiang Zhai
• Keywords: Improve Retrieval Accuracy, Information Management, Mutual Information-based Estimation, SIGIR 2010, Translation Model