We present a method for learning bilingual translation lexicons from monolingual corpora. Word types in each language are characterized by purely monolingual features, such as context counts and orthographic substrings. Translations are induced using a generative model based on canonical correlation analysis, which explains the monolingual lexicons in terms of latent matchings. We show that high-precision lexicons can be learned in a variety of language pairs and from a range of corpus types.
Unsupervised cross-lingual learning REF were first to explore unsupervised BDI, using features such as context counts and orthographic substrings, and canonical correlation analysis.
Learning Bilingual Lexicons from Monolingual Corpora
Annual Meeting Of The Association For Computational Linguistics
Computer Science