Training and evaluation of vector models for Galician

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AMCTHGAZU" target="_blank" >RIV/00216208:11320/25:MCTHGAZU - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195503004&doi=10.1007%2fs10579-024-09740-0&partnerID=40&md5=1ce2cd5f6ef74d8286fbf1ddd9afa46e" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195503004&doi=10.1007%2fs10579-024-09740-0&partnerID=40&md5=1ce2cd5f6ef74d8286fbf1ddd9afa46e</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10579-024-09740-0" target="_blank" >10.1007/s10579-024-09740-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Training and evaluation of vector models for Galician

Popis výsledku v původním jazyce

This paper presents a large and systematic assessment of distributional models for Galician. To this end, we have first trained and evaluated static word embeddings (e.g., word2vec, GloVe), and then compared their performance with that of current contextualised representations generated by neural language models. First, we have compiled and processed a large corpus for Galician, and created four datasets for word analogies and concept categorisation based on standard resources for other languages. Using the aforementioned corpus, we have trained 760 static vector space models which vary in their input representations (e.g., adjacency-based versus dependency-based approaches), learning algorithms, size of the surrounding contexts, and in the number of vector dimensions. These models have been evaluated both intrinsically, using the newly created datasets, and on extrinsic tasks, namely on POS-tagging, dependency parsing, and named entity recognition. The results provide new insights into the performance of different vector models in Galician, and about the impact of several training parameters on each task. In general, fastText embeddings are the static representations with the best performance in the intrinsic evaluations and in named entity recognition, while syntax-based embeddings achieve the highest results in POS-tagging and dependency parsing, indicating that there is no significant correlation between the performance in the intrinsic and extrinsic tasks. Finally, we have compared the performance of static vector representations with that of BERT-based word embeddings, whose fine-tuning obtains the best performance on named entity recognition. This comparison provides a comprehensive state-of-the-art of current models in Galician, and releases new transformer-based models for NER. All the resources used in this research are freely available to the community, and the best models have been incorporated into SemantiGal, an online tool to explore vector representations for Galician. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

Název v anglickém jazyce

Training and evaluation of vector models for Galician

Popis výsledku anglicky

This paper presents a large and systematic assessment of distributional models for Galician. To this end, we have first trained and evaluated static word embeddings (e.g., word2vec, GloVe), and then compared their performance with that of current contextualised representations generated by neural language models. First, we have compiled and processed a large corpus for Galician, and created four datasets for word analogies and concept categorisation based on standard resources for other languages. Using the aforementioned corpus, we have trained 760 static vector space models which vary in their input representations (e.g., adjacency-based versus dependency-based approaches), learning algorithms, size of the surrounding contexts, and in the number of vector dimensions. These models have been evaluated both intrinsically, using the newly created datasets, and on extrinsic tasks, namely on POS-tagging, dependency parsing, and named entity recognition. The results provide new insights into the performance of different vector models in Galician, and about the impact of several training parameters on each task. In general, fastText embeddings are the static representations with the best performance in the intrinsic evaluations and in named entity recognition, while syntax-based embeddings achieve the highest results in POS-tagging and dependency parsing, indicating that there is no significant correlation between the performance in the intrinsic and extrinsic tasks. Finally, we have compared the performance of static vector representations with that of BERT-based word embeddings, whose fine-tuning obtains the best performance on named entity recognition. This comparison provides a comprehensive state-of-the-art of current models in Galician, and releases new transformer-based models for NER. All the resources used in this research are freely available to the community, and the best models have been incorporated into SemantiGal, an online tool to explore vector representations for Galician. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

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

2024

Kód důvěrnosti údajů

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

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

44

Strana od-do

1419-1462

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85195503004