Inside the framework of robust parsers for the syntactic analysis of
unrestricted text, the aim of this work is the construction of a system
capable of automatically learning Constraint Grammar rules from a POS
annotated Corpus. The system presented is able by now to acquire constraint
rules for POS tagging and we plan to extend it to cover syntactic rules.
The learning process uses a supervised learning algorithm based on
building a discrimination forest, with a decision tree attached to each
case of POS ambiguity. The system has been applied to four representative
cases of ambiguity performing on a Spanish Corpus. The results obtained
in these experiments and some discussion about the appropriateness of the
proposed learning technique are presented in this paper.
CitationMarquez, L., Rodriguez, H. "Towards learning a constraint grammar from annotated corpora using decision trees". 1996.
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