Word clustering with parallel spoken language corpora
We introduce a word clustering algorithm which uses a bilingual, parallel corpus to group together words in the source and target language. Our method generalizes previous mutual information clustering algorithms for monolingual data by incorporating a statistical translation model. Preliminary experiments have shown that the algorithm can effectively employ the constraints implicit in bilingual data to extract classes which are well suited to machine translation tasks.