This paper presents a technique that employs linguistic resources to refine PoS tagging using the Universal Dependencies (UD) model. The technique is based on the development and use of lists of non-ambiguous single tokens and non-ambiguous co-occuring tokens in Portuguese (regardless of whether they constitute multiword expressions or not). These lists are meant to automatically correct the tags for such tokens after tagging. The technique is applied over the output of two well-known state of the art systems - UDPipe and UDify - and the results for a real data set have shown a significant improvement of annotation accuracy. Overall, we improve tagging accuracy by up to 1.4%, and, in terms of the number of fully correct tagged sentences, our technique produces results that are 13.9% more accurate than the corresponding original system.
CITATION STYLE
Lopes, L., Duran, M. S., & Pardo, T. A. S. (2021). Universal Dependencies-Based PoS Tagging Refinement Through Linguistic Resources. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13074 LNAI, pp. 601–615). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-91699-2_41
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