Training and evaluation of vector models for Galician

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Training and evaluation of vector models for Galician

Original language description

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.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

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)

Project

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Continuities

—

Publication year

2024

Confidentiality

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

Name of the periodical

Language Resources and Evaluation

ISSN

1574-020X

e-ISSN

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Volume of the periodical

2024

Issue of the periodical within the volume

2024

Country of publishing house

US - UNITED STATES

Number of pages

44

Pages from-to

1419-1462

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85195503004