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Feature Words Selection for Knowledge-based Word Sense Disambiguation with Syntactic Parsin

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Warianty tytułu
PL
Selekcja cech słowa dla jednoznacznego wykrywania znaczenia z syntaktyczną analizą składni
Języki publikacji
EN
Abstrakty
EN
Feature words are crucial clues for word sense disambiguation. There are two methods to select feature words: window-based and dependency-based methods. Both of them have some shortcomings, such as irrelevant noise words or paucity of feature words. In order to solve the problems of the existing methods, this paper proposes two methods to select feature words with syntactic parsing, which are based on phrase structure parsing tree (PTree) and dependency parsing tree (DTree). With the help of syntactic parsing, the proposed methods can select feature words more accurately, which can alleviate the effect of noise words of window-based method and can avoid the paucity of feature words of dependency-based method. Evaluation is performed on a knowledge-based WSD system with a publicly available lexical sample dataset. The results show that both of the proposed methods are superior to window-based and dependency-based methods, and the method based on PTree is better than the method based on DTree. Both of them are preferred strategies to select feature words to disambiguate ambiguous words.
PL
W artykule zaproponowano dwie metody selekcji cech słowa bazujące na analizie składni struktury frazy oraz analizie składni zależności. Badania przeprowadzono przy wykorzystaniu rożnych baz danych. Proponowana metoda ma większą dokładność niż dotychczas stosowane metody: okna i zależności.
Rocznik
Strony
82--87
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wykr.
Twórcy
autor
autor
autor
  • School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China, luwpeng@bit.edu.cn
Bibliografia
  • [1] P. Resnik. WSD in NLP Applications, in Word Sense Disambiguation : Algorithms and applications, (Springer, Berlin/Heidelberg, 2007), 299-337.
  • [2] J. R. Firth. A synopsis of linguistic theory 1930-55. London: The Philological Society, (1957).
  • [3] P. Jin. Researches on Some Key Issues of Word Sense Disambiguation. PhD dissertation. Peking University, (2009).
  • [4] R. Koeling, D. McCarthy and J. Carroll. Domain-Specific Sense Distributions and Predominant Sense Acquisition. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP). ACL, (2005), 419-426.
  • [5] E. Agirre and O. L. d. Lacalle. On Robustness and Domain Adaptation using SVD for Word Sense Disambiguation. In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008). ACL, (2008), 17-24.
  • [6] T. Pedersen. Unsupervised Corpus-based Methods for WSD, in Word Sense Disambiguation : Algorithms and applications, (Springer Verlag, Berlin/Heidelberg, 2007), 133-166.
  • [7] D. Yarowsky. Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd Annual Meeting on Association For Computational Linguistics. ACL, (1995), 189-196.
  • [8] D. McCarthy, R. Koeling, JulieWeeds and J. Carroll. Unsupervised Acquisition of Predominant Word Senses. Computational Linguistics, 33 (2007)4, 553-590.
  • [9] E. Agirre, O. L. d. Lacalle and A. Soroa. Knowledge-based WSD and specific domains: performing over supervised WSD. In Proceedings of the International Joint Conference on Artificial Intelligence 2009. AAAI Press, (2009), 1501-1506.
  • [10] S. Patwardhan, S. Banerjee and T. Pedersen. Using measures of semantic relatedness for word sense disambiguation. In Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics. Springer, (2003), 241-257.
  • [11] S. Patwardhan, S. Banerjee and T. Pedersen. UMND1: Unsupervised Word Sense Disambiguation Using Contextual Semantic Relatedness. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007). ACL, (2007), 390-393.
  • [12] T. Pedersen and V. Kolhatkar. WordNet::SenseRelate::AllWords -A Broad CoverageWord Sense Tagger that Maximizes Semantic Relatedness. In Proceedings of NAACL HLT 2009. ACL, (2009), 17-20.
  • [13] D. Lin. Automatic retrieval and clustering of similar words. In Proceedings of COLING-ACL 98. ACL, (1998), pp. 768-774.
  • [14] Z. Lu, G. Zhang and S. Li. Word Sense Disambiguation Based on Dependency Relationship Analysis and Bayes Model. High Technology Letters, 13 (2003)5, 1-7.
  • [15] P. Chen, W. Ding, C. Bowes and D. Brown. A Fully Unsupervised Word Sense Disambiguation Method Using Dependency Knowledge. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the ACL. ACL, (2009), 28-36.
  • [16] N. Chomsky. Three models for the description of language. Institute of Radio Engineers Transactions on Information Theory 2, (1956)113-124.
  • [17] L. Tesnière. Éléments de syntaxe structurale. Paris: Klincksieck, (1959).
  • [18] M.-C. d. Marneffe, B. MacCartney and C. D. Manning. Generating Typed Dependency Parses from Phrase Structure Parses. In Proceedings of 5th International Conference on Language Resources and Evaluation (LREC 2006). ELRA, (2006), 449-454.
  • [19] H. Huang and W. Lu. Knowledge-based Word Sense Disambiguation with Feature Words Based on Dependency Relation and Syntax Tree. International Journal of Advancements in Computing Technology, 3 (2011)8, 73-81.
  • [20] WordNet::Similarity, (Accessed at http://wnsimilarity. sourceforge.net/).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOB-0049-0018
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