A new approach for unsupervised word sense disambiguation in Hindi language using graph connectivity measures Online publication date: Sat, 29-Nov-2014
by Amita Jain; D.K. Lobiyal
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 4, No. 4, 2014
Abstract: Word sense disambiguation (WSD) is an important task in computational linguistics as it is essential for many language understanding applications. In this paper, we propose a graph-based unsupervised WSD method for Hindi text which disambiguates multiple ambiguous words present in the sentence simultaneously. In our approach, we first construct the semantic graph for each interpretation of the given sentence by establishing semantic relations between the pair of words present in the sentence. We use Hindi WordNet to establish semantic relations between the pair of words and then we construct the graph. We find the cost of spanning tree corresponding to each semantic graph and the interpretation for which spanning tree has the minimum cost is identified. This interpretation is considered as the resulting interpretation. Our approach also considers all open class words unlike the previous approaches which focus only on noun.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Artificial Intelligence and Soft Computing (IJAISC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook