Robust measurement and comparison of context similarity for finding translation pairs

In cross-language information retrieval it is often important to align words that are similar in meaning in two corpora writ-ten in different languages. Previous re-search shows that using context similar-ity to align words is helpful when no dictionary entry is available. We sug-gest a new method which selects a sub-set of words (pivot words) associated with a query and then matches these words across languages. To detect word associa-tions, we demonstrate that a new Bayesian method for estimating Point-wise Mutual Information provides improved accuracy. In the second step, matching is done in a novel way that calculates the chance of an accidental overlap of pivot words us-ing the hypergeometric distribution. We implemented a wide variety of previously suggested methods. Testing in two con-ditions, a small comparable corpora pair and a large but unrelated corpora pair, both written in disparate languages, we show that our approach consistently out-performs the other systems.