Universal Dependencies-Based PoS Tagging Refinement Through Linguistic Resources
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10441780" target="_blank" >RIV/00216208:11320/21:10441780 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-91699-2_41" target="_blank" >https://doi.org/10.1007/978-3-030-91699-2_41</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-91699-2_41" target="_blank" >10.1007/978-3-030-91699-2_41</a>
Alternative languages
Result language
angličtina
Original language name
Universal Dependencies-Based PoS Tagging Refinement Through Linguistic Resources
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN
978-3-030-91698-5
ISSN
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e-ISSN
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Number of pages
15
Pages from-to
601-615
Publisher name
Springer
Place of publication
Berlin
Event location
online
Event date
Nov 29, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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