Matching meaning for cross-language information retrieval

This article describes a framework for cross-language information retrieval that efficiently leverages statistical estimation of translation probabilities. The framework provides a unified perspective into which some earlier work on techniques for cross-language information retrieval based on translation probabilities can be cast. Modeling synonymy and filtering translation probabilities using bidirectional evidence are shown to yield a balance between retrieval effectiveness and query-time (or indexing-time) efficiency that seems well suited large-scale applications. Evaluations with six test collections show consistent improvements over strong baselines.

► We describe a framework for cross-language information retrieval.
► The framework leverages statistical estimation of translation probabilities.
► It models synonymy and bidirectional translation knowledge.
► It yields a balance between retrieval effectiveness and efficiency.
► Evaluations show consistent improvements over strong baselines.