Using a Probabilistic Translation Model for Cross-Language Information Retrieval

Abst rac t There is an increasing need for document search mechanisms capable of matching a natural language query with documents written in a different language. Recently, we conducted several experiments aimed at comparing various methods of incorporating a cross-linguistic capability to existing information retrieval (IR) systems. Our results indicate that translating queries with off-theshelf machine translation systems can result in relatively good performance. But the results also indicate that other methods can perfonn even better. More specifically, we tested a probabilistic translation model of the kind proposed by Brown & al. [2]. The parameters of that system had been estimated automatically on a different, unrelated, corpus of parallel texts. After we augmented it with a small bilingual dictionary, this probabilistic translation model outperformed machine translation systems on our cross-language IR task.