Abstract
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 Dependencies 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 evaluation 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.
Supported by the Walloon Region (Projects BEWARE 1510637 and 1610378) and Altissia International.
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Notes
- 1.
The parser model, along with the material that was used in this paper can be found in https://cental.uclouvain.be/resources/smalla_smille/passport/.
- 2.
- 3.
By the time of the execution of the experiments in this paper, the available PT-UD corpus was in its version 2.1.
- 4.
For instance, the tag DET in the short POS appears as DET or ART in the long POS, while the tag DET in the long POS appears as DET or PRON in the short POS.
- 5.
This modified version of the corpus is available along with the parser model at the PassPort website https://cental.uclouvain.be/resources/smalla_smille/passport/.
- 6.
We detected some fluctuation in the scores during preliminary testing.
- 7.
Zeman et al. [23] argue that larger dimensions may yield better results for parsing.
- 8.
The best system was run five times with randomized train and test sets.
- 9.
Using the most recent PT-UD corpus (version 2.2) in similar setups, we also had a better performance using long POS information over short POS.
- 10.
- 11.
Although there are 30 sentences selected from each genre, in the results, it is possible to observe that both parsing systems (PassPort and PALAVRAS) use their own sentence splitters, so that the final sentence numbers are different (for instance, PALAVRAS splits sentences when there is a colon).
- 12.
Selected romances from www.dominiopublico.gov.br.
- 13.
This corpus was compiled in the scope of the project PorPopular (www.ufrgs.br/textecc/porlexbras/porpopular/index.php).
- 14.
We did not evaluate punctuation tokens, since PALAVRAS does not provide dependency label for them and, in both parsing models, they are simply attached to the root or the closest dependency to the root.
- 15.
This is not in line with the UD guidelines (universaldependencies.org/u/dep/iobj.html), which indicate that the indirect objects should be marked as obj (if they are the sole object of the verb) or as iobj (if there is another obj in the clause). According to the guidelines, obl should only be used for adjuncts, but that is not the case in the PT-UD corpus.
- 16.
The tags present also a < or > symbol, which indicates the attachment direction.
- 17.
The model, training datasets and evaluation files will be made available with the final version.
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Zilio, L., Wilkens, R., Fairon, C. (2018). PassPort: A Dependency Parsing Model for Portuguese. In: Villavicencio, A., et al. Computational Processing of the Portuguese Language. PROPOR 2018. Lecture Notes in Computer Science(), vol 11122. Springer, Cham. https://doi.org/10.1007/978-3-319-99722-3_48
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