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Extracting Brazilian Portuguese noun phrases from texts with TBL

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Konferencja
Human Language Technologies as a challenge for Computer Science and Linguistics (2; 21-23.04.2005; Poznań, Poland)
Języki publikacji
EN
Abstrakty
EN
This paper describes a Transformation Based Learning (TBL) noun phrase extractor for Portuguese. We discuss the reasons for variation in performance between experiments with Portuguese and with English, taking special notice of the linguistic differences between the two languages with respect to noun phrases. latin languages such as Spanish, French and Italian will present the same problems, and could benefit from the same analysis presented here.
Rocznik
Strony
613--623
Opis fizyczny
Bibliogr. 16 poz., tab.
Twórcy
autor
  • Departamento de Engenharia de Sistemas, Instituto Militar de Engenharia, 80, Rio de Janeiro, 22290-270 Brazil
  • Departamento de Informatica, PUC-Rio Rua Margues de Sao Vicente, Rio de Janeiro, Brazil
Bibliografia
  • [1] E. Bechara: Moderna Gramatica Portuguesa. Ed. Nacional, 1976.
  • [2] E. Bick: The Parsing System Palavras: Automatic Grammatical Analysis of Portuguese in a Constraint Grammar Framework. PhD thesis, Aarhus University. 2000.
  • [3] E. Brill: Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging. Computational Linguistics, 21(4) (1995). 543-565.
  • [4] E. Brill and P. Resnik: A rule-based approach to prepositional phrase attachment disambiguation. In Proc. of COLING'94, Kyoto, Japan, 1994.
  • [5] S. Cortson-Oliver and M. Gamon: Combining decision trees and transformation-based learning to correct transferred linguistic representations. In Proc. Ninth Machine Tranlsation Summit, New Orleans, USA, (2003) 55-62. Association for Machine Translation in the Americas.
  • [6] M. C. de Freitas, M. Uzeda-Garrao, C. Oliveira, C. Nogueira Dos Santos and M. C. Silveira: A anotacao de um corpus para o aprendizado supervisionado de urn modelo de sn. In Anais do XXV Congresso da Sociedade Brasileira de Computacao, Brazil. (2005).
  • [7] F. Radu, J. Henderson and G. Ngai: Coaxing confidence from an old friend: Probabilistic classifications from transformation rule lists. In Proc. Joint Sigdat Conf. on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-2000), Hong Kong University of Science and Technology. October. (2000).
  • [8] M. Hepple: Independence and commitment:assumptions for rapid training and execution of rule-based pos taggers. In Proc. 38th Animal Meeting of the ACL, Hong Kong, (2000). 278-285. Association for Computational Linguistics.
  • [9] M. H. Mateus, A. M. Brito. L Duarte and I. H. Faria: Gramática da lingua portuguesa. Ed. Caminho, Lisboa. 4th edition. 1994.
  • [10] B. Megyesi: Shallow parsing with pos taggers and linguistic features. J. of Machine Learning Research, 2 (2002). 639-668.
  • [11] G. Ngai and F. Radu Florian: Transformation-based learning in the last lane. hi Proc. North American Chapter of the Association for Computational Linguistics, (2001), 40-47.
  • [12] A. Radford: Transformational Syntax. Cambridge University Press, 1981.
  • [13] L. Ramshaw and M. Marcus: Text chunking using transformation-based learning. In David Yarovsky and Kenneth Church. (Eds). Proc. Third Workshop on Very Large Corpora, New Jersey. USA, (1995), 82-94. Association for Computational Linguistics.
  • [14] E. Roche and Y. Schabes: Deterministic part-of-speech tagging with finite-state transducers. Computational Linguistics. 21(2). (1995), 227-253.
  • [15] S. Ken: Lazy transformation-based learning. In Proc. 11th: Int. Florida Artificial Intelligence Research Symposium Conference. Florida. USA. (1998), 235-239.
  • [16] G. Satta and E. Brill: Efficient transformation-based parsing, In Proc. 34th Conf. on Association for Computational Linguistics, California. USA. (1996), 255-262. Association for Computational Linguistics.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW3-0021-0030
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