Using Statistical Translation Models for Bilingual IR

This report describes our test on using statistical translation models for bilingual IR tasks in CLEF-2001. These translation models have been trained on a set of parallel web pages automatically mined from the Web. Our goal is to compare the following approaches: using the original parallel corpora or a cleaned corpora to train translation models; using the raw translation probabilities to weigh query words or combine the probabilities with IDF; using different cut-off probability values in the translation models (i.e. delete the translations lower than a threshold). Our results show that: the models trained on the original parallel corpus work better than those on the cleaned corpora; the combination of the probabilities with IDF is beneficial; and it is better to cut-off the translation models at a certain value (0.01 in our case) than not cut them.