Using ‘Low-cost’ Learning Features for Pronoun Resolution
Ramon Ré Moya Cuevas, Ivandré Paraboni · 2008
We investigate a machine learning approach to Portuguese pronoun resolution. We presently focus on so-called ‘low-cost’ learning features readily obtainable from the output of a part-of-speech tagger, and we largely bypass deep syntactic and semantic analysis. Preliminary results show significant improvement in resolution precision and recall, and are comparable to existing rule-based approaches for the Portuguese language spoken in Brazil.