Training and scaling preference functions for disambiguation
Computational Linguistics
Ambiguity packing in constraint-based parsing: practical results
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Preference semantics, ill-formedness, and metaphor
Computational Linguistics - Special issue on ill-formed input
Coping with ambiguity in a large-scale machine translation system
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Syntactic preferences for robust parsing with semantic preferences
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
| Hi-index | 0.00 |
This paper describes the rule-based approach for ambiguity resolution used by English sentence parser in E-TRAN 2001, an English to Korean machine translation system. Parser's Ambiguity Type Information (PATI) is used to automatically identify the types of ambiguities observed in competing candidate trees produced by the parser and summarizes the types into a formal representation. PATI provides an efficient way of encoding knowledge into grammar rules and calculating rule preference scores from a relatively small training corpus. We compare the enhanced grammar with the initial one in view of the amount of ambiguity. The experimental results show that the rule preference scores could significantly increase the accuracy of ambiguity resolution.