A main challenge in applying translation language models to information retrieval is how to estimate the "true" probability that a query could be generated as a translation of a document. The state-of-art methods rely on document-based word co-occurrences to estimate word-word translation probabilities. However, these methods do not take into account the proximity of co-occurrences. Intuitively, the proximity of co-occurrences can be exploited to estimate more accurate translation probabilities, since two words occur closer are more likely to be related. In this paper, we study how to explicit...
