A machine learning approach to the identification of appositives

Authors:
Maria Claudia Freitas;Julio C. Duarte;Cícero N. Santos;Ruy L. Milidiú;Raúl P. Rentería;Violeta Quental
Affiliations:
Departamento de Letras, Pontifícia Universidade Católica, Rio de Janeiro, Brazil;Centro Tecnológico do Exército, Rio de Janeiro, Brazil;Departamento de Informática, Pontifícia Universidade Católica, Rio de Janeiro, Brazil;Departamento de Informática, Pontifícia Universidade Católica, Rio de Janeiro, Brazil;Departamento de Informática, Pontifícia Universidade Católica, Rio de Janeiro, Brazil;Departamento de Letras, Pontifícia Universidade Católica, Rio de Janeiro, Brazil
Venue:
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Year:
2006

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Abstract

Appositives are structures composed by semantically related noun phrases. In Natural Language Processing, the identification of appositives contributes to the building of semantic lexicons, noun phrase coreference resolution and information extraction from texts. In this paper, we present an appositive identifier for the Portuguese language. We describe experimental results obtained by applying two machine learning techniques: Transformation-based learning (TBL) and Hidden Markov Models (HMM). The results obtained with these two techniques are compared with that of a full syntactic parser, PALAVRAS. The TBL-based system outperformed the other methods. This suggests that a machine learning approach can be beneficial for appositive identification, and also that TBL performs well for this language task.