Universal Dependencies-Based PoS Tagging Refinement Through Linguistic Resources

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Universal Dependencies-Based PoS Tagging Refinement Through Linguistic Resources

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Universal Dependencies-Based PoS Tagging Refinement Through Linguistic Resources

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-3-030-91698-5

ISSN

—

e-ISSN

—

Počet stran výsledku

15

Strana od-do

601-615

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

online

Datum konání akce

29. 11. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—