Abstract
The chapter develops an artificial neural network (ANN) based strategy to identify the sense of an ambiguous word as part of an information retrieval mechanism in a question answering system. The philosophy echoes to support the broad domain of natural language processing through its trained capabilities and endeavour to resolve the lexical ambiguity. The neural network is designed using the relations between the target word and associated words that appear in the sentence. It evolves a feed forward procedure to allow the weights to be adjusted and arrive at the precise contextual sense of the word. The inputs and weights are assigned in tune with the frequently appearing words in the English literature to train the model. The performance is investigated for a set of words that inherit a similar context with the words used in the training phase and the results claim the emergence of a promising word sense identifying tool.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amsler R, Walker D (1986) The use of machine readable dictionaries in sublanguage analysis. In: Grishman R, Kittredge R (eds) Analyzing language in restricted domains. LEA Press, Germany pp 69–83
Yarowsky D (1995) Unsupervised word sense disambiguation rivaling supervised methods. In: Proceedings of the 33rd annual meeting of the association for computational linguistics (ACL), Cambridge, pp 189–196
Resnik P (1995) Disambiguating noun groupings with respect to wordNet senses. In: Proceedings of the third workshop on very large corpora, MIT
Resnik P (1999) Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res 11:95–130
Wilks Y, Fass D, Guo C, McDonal J, Plate T, Slator B (1993) Providing machine tractablle dictionary tools. In: Pustejovsky J (ed) semantics and the lexicon, pp 341–401
Agirre E, Rigau G (1996) Word sense disambiguation using conceptual density. In: Proceedings of the 16th International Conference on Computational Linguistics (COLING), Copenhagen
Lesk M (1986) Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proceedings of the 5th annual international conference on Systems documentation, Toronto
Krovetz R, Bruce Croft W (1992) Lexical ambiguity and information retrieval. Inf Syst 10(2):115–141
Yarowsky D (1992) Word sense disambiguation using statistical models of Roget’s categories trained on large corpora. In: Proceedings of the 14th international conference on computational linguistics (COLING), Nantes, pp 454–460
Sussna M (1993) Word sense disambiguation for free-text indexing using a massive semantic network. In: Proceedings of the second international conference on information and knowledge management, Arlington
Watanabe N, Ishizaki S (2007) Neural network model for word sense disambiguation using up/down state and morphoelectrotonic transform. J Adv Comput Intell Intell Inf 11(7): 780–786
Mercer RL (1993) The mathematics of statistical machine translation. Comput Linguist 19(2):263–331
Tanaka T, Bond F, Baldwin T, Fujita S, Hashimoto C (2007) Word sense disambiguation incorporating lexical and structural semantic information. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning, pp 477–485, Prague, (Association for Computational Linguistics) June 2007
Meenakshi C, Thangaraj P, Ramasamy M (2011) A novel scheme to identify the word sense in question answering systems. Int J Comput Sci Telecommun 2(9): 26–29
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer India
About this paper
Cite this paper
Meenakshi, C., Thangaraj, P. (2013). ANN Based Word Sense Identifying Scheme for Question Answering Systems. In: Malathi, R., Krishnan, J. (eds) Recent Advancements in System Modelling Applications. Lecture Notes in Electrical Engineering, vol 188. Springer, India. https://doi.org/10.1007/978-81-322-1035-1_44
Download citation
DOI: https://doi.org/10.1007/978-81-322-1035-1_44
Published:
Publisher Name: Springer, India
Print ISBN: 978-81-322-1034-4
Online ISBN: 978-81-322-1035-1
eBook Packages: EngineeringEngineering (R0)