Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(142.45KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
An Improved Approach for Word Ambiguity Removal
Priti R Saktel, Urmila Shrawankar
Pages - 71 - 82     |    Revised - 15-09-2012     |    Published - 25-10-2012
Volume - 3   Issue - 3    |    Publication Date - October 2012  Table of Contents
MORE INFORMATION
KEYWORDS
Human Computer Interaction, Supervised Training, Unsupervised Learning, Word Ambiguity, Word Sense Disambiguation
ABSTRACT
Word ambiguity removal is a task of removing ambiguity from a word, i.e. correct sense of word is identified from ambiguous sentences. This paper describes a model that uses Part of Speech tagger and three categories for word sense disambiguation (WSD). Human Computer Interaction is very needful to improve interactions between users and computers. For this, the Supervised and Unsupervised methods are combined. The WSD algorithm is used to find the efficient and accurate sense of a word based on domain information. The accuracy of this work is evaluated with the aim of finding best suitable domain of word. Keywords: Human Computer Interaction, Supervised Training, Unsupervised Learning, Word Ambiguity, Word sense disambiguation
CITED BY (1)  
1 Saktel, P., Domke, M., & Vilhekar, L. Performance Improvement of Context Identification for Human Computer Interaction.
1 Google Scholar 
2 CiteSeerX 
3 Scribd 
4 SlideShare 
5 PdfSR 
1 Roberto Navigli, Mirella Lapata (2010), “An Experimental Study of graph connectivity for unsupervised word sense disambiguation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 4.
2 Myunggwon Hwang, Chang Choi (2011),” Automatic Enrichment of Semantic Relation Network and Its application to Word sense Disambiguation”,IEEE transaction on knowledge and data engineering, vol. 23, no. 6.
3 Francisco Tacoa, Hiroshi Uchida (2010),”A Word Sense Disambiguation approach for converting Natural Language Text into a Common Semantic Description", Fourth International Conference on Semantic Computing, IEEE.
4 Ping Chen, Wei Ding (2010), “Word Sense Disambiguation with Automatically Acquired Knowledge”, IEEE Intelligent Systems.
5 Ling Che Yangsen Zhang (2011),”Study on Word Sense Disambiguation Knowledge base based on Multi-Sources”, IEEE.
6 Alex Roney Mathew; Al Hajj (2011), “Human-Computer Interaction (HCI): An overview”,IEEE International Conference on Computer Science and Automation Engineering(CSAE).
7 E. Agirre and P. Edmonds (2006),” Word sense Disambiguation: Algorithms and Applications”, Springer.
8 Johan Bos and Malvina Nissim (2009),” From shallow to deep Natural language processing: A hands-on tutorial”, Springer.
9 Leung (2006),“Learners as users, and users as learners”, 7th International Conference on Information Technology Based Higher Education and Training, ITHET '06.
10 Yousif, J.H. Sembok, T.(2008),”Arabic part-of-speech tagger based Support Vectors Machines”, Information Technology,2008. ITSim 2008. International Symposium.
11 Diana McCarthy, Rob Koaling (2007),”Unsupervised Acquisition of Predominant Word Senses,” Computational Linguistics, Vol.33, No.4.
12 Andrei Minca, Stefan Diaconescu (2011),”An Approach to Knowledge-Based Word Sense Disambiguation Using Semantic Trees Built on a WordNet Lexicon Network”,IEEE.
13 Priti Saktel,Urmila Shrawankar(2012),”Context based Meaning Extraction for HCI Using WSD Algorithm:A Review”, IEEE-International Conference on Advances in Engineering,Science and Management,pp. 208-212.
14 Jerome R. Bellegarda, Fellow (2010),” Part-of-Speech Tagging by Latent Analogy”, IEEE Journal of Selected Topics In Signal Processing, Vol. 4, No. 6, Dec. 2010.
Miss Priti R Saktel
GHRCE - India
saktel.priti10@rediffmail.com
Dr. Urmila Shrawankar
G. H. Raisoni College of Engineering Nagpur, - India