  Topic models have been successfully applied in lexicon extraction. However,
most previous methods are limited to document-aligned data. In this paper, we
try to address two challenges of applying topic models to lexicon extraction in
non-parallel data: 1) hard to model the word relationship and 2) noisy seed
dictionary. To solve these two challenges, we propose two new bilingual topic
models to better capture the semantic information of each word while
discriminating the multiple translations in a noisy seed dictionary. We extend
the scope of topic models by inverting the roles of "word" and "document". In
addition, to solve the problem of noise in seed dictionary, we incorporate the
probability of translation selection in our models. Moreover, we also propose
an effective measure to evaluate the similarity of words in different languages
and select the optimal translation pairs. Experimental results using real world
data demonstrate the utility and efficacy of the proposed models.
