Abstract
Word Sense Disambiguation (WSD) has been an important and difficult problem in Natural Language Processing (NLP) for years. This paper proposes a novel WSD method which expands the knowledge for senses of ambiguous word through semantic knowledge in WordNet. First, selecting feature words through syntactic parsing. Second, expanding the knowledge for the ambiguous word senses through glosses and structured semantic relations in WordNet. Third, computing the semantic relevancy between ambiguous word and context and achieving the purpose of WSD by semantic network in WordNet. Lastly, adopting the Senseval-3 all words data sets as the test set to evaluate our approach. Through a detailed experimental evaluation, the result shows that our approach achieves improvements over some classical methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Information on http://wordnet.princeton.edu
Budanitsky A, Hirst G (2006) Evaluating wordnet-based measures of lexical semantic relatedness. Comput Linguist 32(1):13–47
Azzini A et al (2009) Evolving neural word sense disambiguation classifiers with a letter-count distributed encoding. In: Artificial life and evolutionary computation, pp 111–120
Nguyen KH, Ock CY (2010) Margin perception for word sense disambiguation. In: SoICT, ACM, pp 64–70
Veenstra J, Van den Bosch A, Buchholz S et al (2000) Memory-based word sense disambiguation. Comput Humanit 34(1–2):171–177
Lee YK, Ng HT, Chia TK (2004) Supervised word sense disambiguation with support vector machines and multiple knowledge sources. In: Senseval-3: third international workshop on the evaluation of systems for the semantic analysis of text, pp 137–140
Navigli R, Lapata M (2010) An experimental study of graph connectivity for unsupervised word sense disambiguation. IEEE Trans Pattern Anal Mach Intell 32(4):678–692
Hessami E, Mahmoudi F, Jadidinejad AH (2011) Unsupervised weighted graph or word sense disambiguation. In: World congress on information and communication technologies (WICI), IEEE, 2011, pp 733–737
Yuret D, Yatbaz MA (2010) The noisy channel model for unsupervised word sense disambiguation. Comput Linguist 36(1):111–127
Chen P, Ding W, Choly M, Bowes C (2012) Word sense disambiguation with automatically acquired knowledge. IEEE Intell Syst 27(4):46–55
Lefever E, Hoste V, De Cock M (2013) Five languages are better than one: an attempt to bypass the data acquisition bottleneck for wsd. In: Computational linguistics and intelligent text processing, Springer, Berlin, pp 343–354
Hwang MG, Choi C, Kim PK (2011) Automatic enrichment of semantic relation network and its application to word sense disambiguation. IEEE Trans Knowl Data Eng 23(6):845–858
Lee WJ, Mit E (2011) Word sense disambiguation by using domain knowledge. In: IEEE 2011 international conference on (STAIR), pp 237–242
Huang H, Lu W (2011) Knowledge-based word sense disambiguation with feature words based on dependency relation and syntax tree. Int J Adv Comput Technol 3(8):73–81
Sinha R, Mihalcea R (2007) Unsupervised graph-based word sense disambiguation using measures of word semantic similarity. In: Proceedings of the IEEE international conference on semantic computing, pp 363–369
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. ACM, pp 24–26
Banerjee S (2002) Adapting the Lesk algorithm for word sense disambiguation to WordNet. University of Minnesota
Chen YQ, Yin J (2005) Sense rank AALesk: a semantic solution for word sense disambiguation. In: Fuzzy systems and knowledge discovery. LNAI 3614. Springer, Heidelberg, pp 710–717
Wenpeng L, Huang H, Zhu C (2012) Feature words selection for knowledge-based word sense disambiguation with syntactic parsing. Przeglad Elektrotechniczny 88(1b):82–87
Acknowledgment
This work is supported by the National Natural Science Foundation of China under Grant (60973040), the National Natural Science Foundation of China under Grant (60903098), the basic scientific research foundation for the interdisciplinary research and innovation project of Jilin University under Grant (201103129) and the Science Foundation for China Post doctor under Grant (2012M510879).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gao, N., Zuo, W., Dai, Y., Lv, W. (2014). Word Sense Disambiguation Using WordNet Semantic Knowledge. In: Wen, Z., Li, T. (eds) Knowledge Engineering and Management. Advances in Intelligent Systems and Computing, vol 278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54930-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-54930-4_15
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54929-8
Online ISBN: 978-3-642-54930-4
eBook Packages: EngineeringEngineering (R0)