A LDA topic Model for Farsi Word Sense Disambiguation

Word sense disambiguation is the task of identifying the correct sense for the Word in a given context among a finite set of possible senses, and plays an important role in many natural language processing applications such as machine translation, document classification, and information retrieval.
In this paper we proposed to use LDA topic model for disambiguating Farsi homograph words with new features. A topic model is a statistical model for extract topics from documents of a corpus. We use Latent Dirichlet Allocation (LDA) that is an unsupervised technique.
The system achieved 97% precision for 4 high frequently Farsi homograph words.