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PassPort: A Dependency Parsing Model for Portuguese

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Zilio, Leonardo [UCL] Souza Wilkens, Rodrigo [UCL] Fairon, Cédrick [UCL]

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.

Document type Contribution à ouvrage collectif (Book Chapter) – Chapitre
Access type Accès libre
Publication date 2018
Language Anglais
Host document Villavicencio, A., Viviane, M., Abad, A., Caseli, H., Gamallo, P., Ramisch, C., Gonçalo Oliveira, H.R., Paetzold, G.H. ; "Computational Processing of the Portuguese Language"- p. 479-489 (ISBN : 978-3-319-99722-3)
Peer reviewed yes
Publication status Soumis
Affiliation UCL - SSH/ILC/PLIN - Pôle de recherche en linguistique
Keywords Dependency parsing ; Parsing performance ; Universal Dependencies ; Parsing for Portuguese
Links
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Bibliographic reference Zilio, Leonardo ; Souza Wilkens, Rodrigo ; Fairon, Cédrick. PassPort: A Dependency Parsing Model for Portuguese. In: Villavicencio, A., Viviane, M., Abad, A., Caseli, H., Gamallo, P., Ramisch, C., Gonçalo Oliveira, H.R., Paetzold, G.H., Computational Processing of the Portuguese Language,  2018, p. 479-489
Permanent URL http://hdl.handle.net/2078.1/208090