Abstract
Within the framework of Transformation Based Learning (TBL), the rule template is one of the most important elements in the learning process. This paper presents a new model for TBL templates, in which the basic unit, denominated here as an atomic term (AT), encodes a variable sized window and a test that precedes the capture of a feature’s value. A case study of Portuguese NP identification is described and the experimental results obtained are presented.
Keywords
- Noun Phrase
- Target Item
- Training Corpus
- Baseline System
- Prepositional Phrase
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ramshaw, L., Marcus, M.: Text chunking using transformation-based learning. In: Yarovsky, D., Church, K. (eds.) Proceedings of the Third Workshop on Very Large Corpora, New Jersey, USA, ACL, pp. 82–94 (1995)
Brill, E.: Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging. Computational Linguistics 21, 543–565 (1995)
Brill, E., Resnik, P.: A rule-based approach to prepositional phrase attachment disambiguation. In: Proceedings of COLING 1994, Kyoto, Japan (1994)
Florian, R., Henderson, J., Ngai, G.: Coaxing confidence from an old friend: Probabilistic classifications from transformation rule lists. In: Proceedings of Joint Sigdat Conference on EMNLP/VLC, Hong Kong (2000)
Megyesi, B.: Shallow parsing with pos taggers and linguistic features. Journal of Machine Learning Research 2, 639–668 (2002)
Hepple, M.: Independence and commitment:assumptions for rapid training and execution of rule-based pos taggers. In: Proceedings of the 38th Annual Meeting of the ACL, Hong Kong, Association for Computational Linguistics, pp. 278–285 (2000)
Ngai, G., Florian, R.: Transformation-based learning in the fast lane. In: Proceedings of North American Chapter of the ACL, pp. 40–47 (2001)
Satta, G., Brill, E.: Efficient transformation-based parsing. In: Proceedings of the 34th conference on Association for Computational Linguistics, California, USA, Association for Computational Linguistics, pp. 255–262 (1996)
Roche, E., Schabes, Y.: Deterministic part-of-speech tagging with finite-state transducers. Computational Linguistics 21, 227–253 (1995)
Santos, C.N.: Aprendizado de máquina na identificação de sintagmas nominais: o caso do português brasileiro. Master’s thesis, IME, Rio de Janeiro - RJ (2005)
Marchi, A.R.: Projeto lacio-web: Desafios na construção de um corpus de 1,1 milhão de palavras de textos jornalísticos em português do brasil. In: 51o Seminário do Grupo de Estudos Lingüísticos do Estado de São Paulo, São Paulo, Brasil (2003)
Bick, E.: The Parsing System Palavras: Automatic Grammatical Analysis of Portuguese in a Constraint Grammar Framework. PhD thesis, Aarhus University (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
dos Santos, C.N., Oliveira, C. (2005). Constrained Atomic Term: Widening the Reach of Rule Templates in Transformation Based Learning. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_61
Download citation
DOI: https://doi.org/10.1007/11595014_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30737-2
Online ISBN: 978-3-540-31646-6
eBook Packages: Computer ScienceComputer Science (R0)