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Tytuł artykułu

Semi-unsupervised PP attachment disambiguation for Norwegian

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Konferencja
Human Language Technologies as a challenge for Computer Science and Linguistics (2; 21-23.04.2005; Poznań, Poland)
Języki publikacji
EN
Abstrakty
EN
Determining the correct attachment site for prepositional phrases is a difficult task for NLP systems. In this work we automatically extract unambiguous PP attachments from a Norwegian corpus and use them for semi-unsupervised training of a memory-based learner on the attachment disambiguation task. The performance of the system is similar to that of a related method which has previously been applied to English, but it obtains this performance level using a simpler and more flexible approach.
Rocznik
Strony
381--392
Opis fizyczny
Bibliogr. 20 poz., tab.
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autor
Bibliografia
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  • [3] E. Brill and P. Resnik: A Rule-Based Approach to Prepositional Phrase Attachment Disambiguation. In Proc. 15th Int. Conf. on Computational Linguistics (COLING-94), Kyoto, Japan, 1994
  • [4] M. Collins and J Brooks: Prepositional attachment through a backed-off model. In D. Yarovsky and K. Church (Eds.). Proc Third Workshop on Very Large Corpora. Somerset. New Jersey Association for Computational Linguistics, (1995).
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  • [7] K. Hagen, J. B. Johannessen and A. Noklestad: A Constraint-Based Tagger for Norwegian. In C.-E Lindberg and S Nordahl Lund (Eds.), 17th Scandinavian Conference of Linguistics, volume I of Odense Working Papers in Language and Communication, Odense, (2000).
  • [8] K. Hagen, J. B. Johannessen and A. Noklestad: A Web-Based Advanced and User Friendly System: The Oslo Corpus of Tagged Norwegian Texts. In M. Gavrilidou, G. Carayannis, S. Markamonatou, S. Piperidis, and G. Stainhaouer (Eds.). Proc. Second Int. Conf., on Language Resources and Evaluation, Athens, Greece, (2000).
  • [9] D. Hlndle and M. Rooth: Structural ambiguity and lexical relations. Computational Linguistics, 19(1), (1993), 103-120.
  • [10] E. Marsi, P.-A. Coppen, C. Gussenhoven and T. Rietveld: Progress in Speech Synthesis, chapter Prosodic and intonational domains in speech synthesis. New York, Springer-Veriag, 1997, pages 477-493.
  • [11] P. Pantel and D. Lin: An Unsupervised Approach to Prepositional Phrase Attachment using Contextually Similar Words. In Proc. Association for Computational Linguistics (ACL-O0), Hong Kong, (2000).
  • [12] S. Della Pietra, V. J. Della Pietra and J. D. Lafferty: Inducing feaiures of random fields. IEEE Trans. Pattern Analysis and Machine Intelligence. 19(4). (1997), 380-393.
  • [13] A. Ratnaparkhi Statistical models for unsupervised prepositional phrase at attachement. In COULIG-ACL (1998).
  • [14] A Ratnaparkhi, J. Reynar and S. Roukos: A Maximum Entropy Model forPrepositional Phrase Attachment In Proc, ARPA Workshop on Human Language Technology, Morgan Kaufmann, (1994)
  • [15] I. A. Sag, T. Baldwin, F. Bond, A. Copestake and D. Flickinger: Multiword expressions: A pain in the neck for NLP. In Proc. 3rd Int. Conf. on Intelligent Text Processing and Computational Linguistics {CICLing-2002), Mexico City. Mexico. (2002).
  • [16] H. Schutze: Ambiguity resolution in language learning. Standford CA, CSLI Publications, (1997).
  • [17] J. Stetina and M. Nagao: Corpus Based PP-Attachment Ambiguity Resolution with a Semantic Dictionary. In J. Zhou and K Church (Eds.), Proc. 5th Workshop on Very Large Corpora. China & Hong Kong. (1997).
  • [18] O. van Herwijnen and J. Terken: Learning PP attachment for filtering prosodic phrasing. In Proc. EACL 2003. 10th Conf. of the European Chapter of the Association for Computational Linguistics, (2003).
  • [19] E. Velldal: Modeling Word Senses With Fuzzy Clustering. Cand. philol. Thesis in language, logic and information. University of Oslo. 2003.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW3-0021-0012
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