Persian Word Sense Disambiguation using LDA topic model

The Word sense disambiguation is a prominent issue in natural language processing. In this paper, a model is proposed for Persian word sense disambiguation using extraction of new features. To generate this model two groups of features are utilized including words and signs accompanying ambiguous word as well as features derived using topic modeling schemes. A topic model is a probabilistic model for extracting abstract of topics which are included in documents of a corpuse. In the paper at hand unsupervised Latent Dirichlet Allocation method is exploited. Experimental results for four ambiguous popular Persian words extracted from research center of intelligent signal processing corpus, show a precision of 939. It demonstrates the effect of this method on finding proper sense of words.

Keywords:

Latent Dirichlet Allocation , multi-sense , word sense disambiguation , topic modeling