Volume 12, Issue 4 (3-2016)
JSDP 2016, 12(4): 117-125 |
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rahati ghochani S. farsi word sense disambiguation with LDA Topic model . JSDP 2016; 12 (4) :117-125
URL: http://jsdp.rcisp.ac.ir/article-1-58-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-58-en.html
Abstract: (7854 Views)
Word sense disambiguation is the task of identifying the correct sense for the word in a given context among a finite set of possible sense. In this paper a model for farsi word sense disambiguation is presented. The model use two group of features: first, all word and stop words around target word and topic models as second features. We extract topics from a farsi corpus with Latent Dirichlet Allocation (LDA) model. The system with a maximum entropy model achieved 97.67% precision for 4 high frequently farsi homograph words
Type of Study: Research |
Subject:
Paper
Received: 2013/06/3 | Accepted: 2016/01/6 | Published: 2016/03/14 | ePublished: 2016/03/14
Received: 2013/06/3 | Accepted: 2016/01/6 | Published: 2016/03/14 | ePublished: 2016/03/14
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