PassPort: A Dependency Parsing Model for Portuguese

Parsers are essential tools for several NLP applications. Here we introduce PassPort, a model for the dependency parsing of Portuguese trained with the Stanford Parser. For developing PassPort, we observed which approach performed best in several setups using different existing parsing algorithms and combinations of linguistic information. PassPort achieved an UAS of 87.55 and a LAS of 85.21 in the Universal Depen- dencies corpus. We also evaluated the model’s performance in relation to another model and different corpora containing three genres. For that, we annotated random sentences from these corpora using PassPort and the PALAVRAS parsing system. We then carried out a manual evalua- tion and comparison of both models. They achieved very similar results for dependency parsing, with a LAS of 85.02 for PassPort against 84.36 for PALAVRAS. In addition, the results from the analysis showed us that better performance in the part-of-speech tagging could improve our LAS.