Abstract
We present the first results for recovering word-word dependencies from a probabilistic parser for Portuguese trained on and evaluated against human annotated syntactic analyses. We use the Floresta Sintá(c)tica with the Bikel multi-lingual parsing engine and evaluate performance on both PARSEVAL and unlabeled dependencies. We explore several configurations, both in terms of parameterizing the parser and in terms of enhancements to the trees used for training the parser. Our best configuration achieves 80.6% dependency accuracy on unseen test material, well above adjacency baselines and on par with previous results for unlabeled dependencies.
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Wing, B., Baldridge, J. (2006). Adaptation of Data and Models for Probabilistic Parsing of Portuguese. In: Vieira, R., Quaresma, P., Nunes, M.d.G.V., Mamede, N.J., Oliveira, C., Dias, M.C. (eds) Computational Processing of the Portuguese Language. PROPOR 2006. Lecture Notes in Computer Science(), vol 3960. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751984_15
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DOI: https://doi.org/10.1007/11751984_15
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