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The paper describes an algorithm that employs English and French text taggers to associate noun phrases in an aligned bilingual corpus. The taggers provide part-of-speech categories which are used by finite-state recognizers to extract simple noun phrases for both languages. Noun phrases are then mapped to each other using an iterative re-estimation algorithm that bears similarities to the Baum-Welch algorithm which is used for training the taggers. The algorithm provides an alternative to other approaches for finding word correspondences, with the advantage that linguistic structure is incorporated. Improvements to the basic algorithm are described, which enable context to be accounted for when constructing the noun phrase mappings.

An algorithm for finding noun phrase correspondences in bilingual corpora