Learning an English-Chinese Lexicon from a Parallel Corpus.

Abstract

We report experiments on automatic learning of an English-Chinese translation lexicon, through statistical training on a large parallel corpus. The learned vocabulary size is nontrivial at 6,517 English words averaging 2.33 Chinese translations per entry, with a manuallyfiltered precision of 95.1% and a single-most-probable precision of 91.2%. We then introduce a significance filtering method that is fully automatic, yet still yields a weighted precision of 86.0%. Learning of translations is adaptive to the domain. To our knowledge, these are the first empirical results of the kind between an Indo-European and non-Indo-European language for any significant corpus size with a non-toy vocabulary. 1 Introduction A criticism of statistical machine translation tools is that convincing empirical results to date are largely confined to similar language pairs, such as French and English. We offer some contributions to the pool of evidence supporting the language-independence of statistical te...