Wednesday 20th of September 2017
 

Probabilistic Latent Semantic Analysis for Unsupervised Word Sense Disambiguation


Gaurav Singh Tomar, Manmeet Singh, Shishir Rai, Atul Kumar, Ratna Sanyal and Sudip Sanyal

This paper presents an unsupervised approach for disambiguating between various senses of a word to select the most appropriate sense, based on the context in the text. We have defined a Probabilistic Latent Semantic Analysis (PLSA) based Word Sense Disambiguation (WSD) system in which sense tagged annotated data is not required for training and the system is language independent giving 83% and 74% accuracy for English and Hindi languages respectively. Also, through word sense disambiguation experiments, we have shown that byapplying Word net in this algorithm, performance of our system can be further enhanced.

Keywords: Word Sense Disambiguation, Probabilistic Latent Semantic Analysis, Word net, Algorithm

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ABOUT THE AUTHORS

Gaurav Singh Tomar
IIIT Allahabad

Manmeet Singh
IIIT Allahabad

Shishir Rai
IIIT Allahabad

Atul Kumar
IIIT Allahabad

Ratna Sanyal
IIIT Allahabad

Sudip Sanyal
IIIT Allahabad


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