Adaptation of discourse parsing models for Portuguese language. Maziero, E. G., Hirst, G., & Pardo, T. A. S. In Proceedings, 4th Brazilian Conference on Intelligent Systems (BRACIS), pages ???--???, Natal, Brazil, November, 2015. abstract bibtex Discourse parsing in Portuguese has two critical limitations. The first is that the task has been explored using only symbolic approaches, i.e., using manually extracted lexical patterns. The second is related to the domain of the lexical patterns, which were extracted through the analysis of a corpus of academic texts, generating many domain-specific patterns. For English, many approaches have been explored using machine learning with features based on a prominent lexicon-syntax notion of dominance sets. In this paper, two works were adapted to Portuguese, improving the results, outperforming the baselines and previous works for Portuguese, considering the task of rhetorical relation identification.
@inproceedings{Maziero2015BRACIS,
author = {Erick Galani Maziero and Graeme Hirst and Thiago A. S. Pardo},
title = {Adaptation of discourse parsing models for Portuguese language},
address = {Natal, Brazil},
booktitle = {Proceedings, 4th Brazilian Conference on Intelligent Systems
(BRACIS)},
pages = {???--???},
year = {2015},
month = {November},
download = {http://ftp.cs.toronto.edu/pub/gh/Maziero-etal-2015-BRACIS.pdf},
abstract = {Discourse parsing in Portuguese has two critical
limitations. The first is that the task has been
explored using only symbolic approaches, i.e., using
manually extracted lexical patterns. The second is
related to the domain of the lexical patterns, which
were extracted through the analysis of a corpus of
academic texts, generating many domain-specific
patterns. For English, many approaches have been
explored using machine learning with features based
on a prominent lexicon-syntax notion of dominance
sets. In this paper, two works were adapted to
Portuguese, improving the results, outperforming the
baselines and previous works for Portuguese,
considering the task of rhetorical relation
identification. }
}
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