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Word Sense Disambiguation Using WordNet Semantic Knowledge

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 278))

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.

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References

  1. Information on http://wordnet.princeton.edu

  2. Budanitsky A, Hirst G (2006) Evaluating wordnet-based measures of lexical semantic relatedness. Comput Linguist 32(1):13–47

    Article  MATH  Google Scholar 

  3. 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

    Google Scholar 

  4. Nguyen KH, Ock CY (2010) Margin perception for word sense disambiguation. In: SoICT, ACM, pp 64–70

    Google Scholar 

  5. Veenstra J, Van den Bosch A, Buchholz S et al (2000) Memory-based word sense disambiguation. Comput Humanit 34(1–2):171–177

    Article  Google Scholar 

  6. 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

    Google Scholar 

  7. 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

    Article  Google Scholar 

  8. 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

    Google Scholar 

  9. Yuret D, Yatbaz MA (2010) The noisy channel model for unsupervised word sense disambiguation. Comput Linguist 36(1):111–127

    Article  Google Scholar 

  10. Chen P, Ding W, Choly M, Bowes C (2012) Word sense disambiguation with automatically acquired knowledge. IEEE Intell Syst 27(4):46–55

    Article  Google Scholar 

  11. 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

    Google Scholar 

  12. 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

    Article  Google Scholar 

  13. Lee WJ, Mit E (2011) Word sense disambiguation by using domain knowledge. In: IEEE 2011 international conference on (STAIR), pp 237–242

    Google Scholar 

  14. 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

    Google Scholar 

  15. 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

    Google Scholar 

  16. 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

    Google Scholar 

  17. Banerjee S (2002) Adapting the Lesk algorithm for word sense disambiguation to WordNet. University of Minnesota

    Google Scholar 

  18. 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

    Google Scholar 

  19. 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

    Google Scholar 

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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).

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Correspondence to Ningning Gao or Wanli Zuo .

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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

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  • DOI: https://doi.org/10.1007/978-3-642-54930-4_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54929-8

  • Online ISBN: 978-3-642-54930-4

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