Towards learning a constraint grammar from annotated corpora using decision trees
Lluís Màrquez Villodre, Horacio Rodríguez Hontoria · 1996
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.