Spoken Spanish PoS tagging: gold standard dataset
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AMF98BC5I" target="_blank" >RIV/00216208:11320/25:MF98BC5I - isvavai.cz</a>
Výsledek na webu
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197288516&doi=10.1007%2fs10579-024-09751-x&partnerID=40&md5=48167acf48572b979c669b95b43a9a03" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197288516&doi=10.1007%2fs10579-024-09751-x&partnerID=40&md5=48167acf48572b979c669b95b43a9a03</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10579-024-09751-x" target="_blank" >10.1007/s10579-024-09751-x</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Spoken Spanish PoS tagging: gold standard dataset
Popis výsledku v původním jazyce
The development of a benchmark for part-of-speech (PoS) tagging of spoken dialectal European Spanish is presented, which will serve as the foundation for a future treebank. The benchmark is constructed using transcriptions of the Corpus Oral y Sonoro del Español Rural (COSER;“Audible corpus of spoken rural Spanish”) and follows the Universal Dependencies project guidelines. We describe the methodology used to create a gold standard, which serves to evaluate different state-of-the-art PoS taggers (spaCy, Stanza NLP, and UDPipe), originally trained on written data and to fine-tune and evaluate a model for spoken Spanish. It is shown that the accuracy of these taggers drops from 0.98-0.99 to 0.94-0.95 when tested on spoken data. Of these three taggers, the spaCy’s trf (transformers) and Stanza NLP models performed the best. Finally, the spaCy trf model is fine-tuned using our gold standard, which resulted in an accuracy of 0.98 for coarse-grained tags (UPOS) and 0.97 for fine-grained tags (FEATS). Our benchmark will enable the development of more accurate PoS taggers for spoken Spanish and facilitate the construction of a treebank for European Spanish varieties. © The Author(s) 2024.
Název v anglickém jazyce
Spoken Spanish PoS tagging: gold standard dataset
Popis výsledku anglicky
The development of a benchmark for part-of-speech (PoS) tagging of spoken dialectal European Spanish is presented, which will serve as the foundation for a future treebank. The benchmark is constructed using transcriptions of the Corpus Oral y Sonoro del Español Rural (COSER;“Audible corpus of spoken rural Spanish”) and follows the Universal Dependencies project guidelines. We describe the methodology used to create a gold standard, which serves to evaluate different state-of-the-art PoS taggers (spaCy, Stanza NLP, and UDPipe), originally trained on written data and to fine-tune and evaluate a model for spoken Spanish. It is shown that the accuracy of these taggers drops from 0.98-0.99 to 0.94-0.95 when tested on spoken data. Of these three taggers, the spaCy’s trf (transformers) and Stanza NLP models performed the best. Finally, the spaCy trf model is fine-tuned using our gold standard, which resulted in an accuracy of 0.98 for coarse-grained tags (UPOS) and 0.97 for fine-grained tags (FEATS). Our benchmark will enable the development of more accurate PoS taggers for spoken Spanish and facilitate the construction of a treebank for European Spanish varieties. © The Author(s) 2024.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
—
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Language Resources and Evaluation
ISSN
1574-020X
e-ISSN
—
Svazek periodika
2024
Číslo periodika v rámci svazku
2024
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
30
Strana od-do
1-30
Kód UT WoS článku
001260434000003
EID výsledku v databázi Scopus
2-s2.0-85197288516