Specifically, we propose a new topic model called Probabilistic Cross-Lingual Latent Semantic Analysis (PCLSA) which extends the Probabilistic Latent Semantic Analysis (PLSA) model by regularizing its likelihood function with soft constraints defined based on a bilingual dictionary.

PCLSA extends the Probabilistic Latent Semantic Analysis (PLSA) model by regularizing its likelihood function with soft constraints defined based on a bilingual dictionary.

In our model, since we only add a soft constraint on word pairs in the dictionary, their probabilities in common topics are generally different, naturally capturing which shows the different variations of a common topic in different languages.

We achieve this by adding such preferences formally to the likelihood function of a probabilistic topic model as “soft constraints” so that when we estimate the model, we would try to not only fit the text data well (which is necessary to extract coherent component topics from each language), but also satisfy our specified preferences (which would ensure the extracted component topics in different languages are semantically related).

corporating the knowledge of a bilingual dictionary as soft constraints .