<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Nowadays, natural language processing (NLP) is increasingly relaying on pre-trained word embeddings for use in various tasks. However, there is little research devoted to Latvian – a language that is much more morphologically complex than English. In this study, several experiments were carried out in three NLP tasks on four different methods of creating word embeddings: word2vec, fastText, Structured Skip-Gram and ngram2vec. The obtained results can serve as a baseline for future research on the Latvian language in NLP. The main conclusions are the following: First, in the part-of-speech task, using a training corpus 46 times smaller than in a previous study, the accuracy was 91.4 % (versus 98.3 % in the previous study). Second, fastText demonstrated the overall best effectiveness. Third, the best results for all methods were observed for embeddings with a dimension size of 200. Finally, word lemmatization generally did not improve results.</p>
</abstract>

Nowadays, natural language processing (NLP) is increasingly relaying on pre-trained word embeddings for use in various tasks. However, there is little research devoted to Latvian – a language that is much more morphologically complex than English. In this study, several experiments were carried out in three NLP tasks on four different methods of creating word embeddings: word2vec, fastText, Structured Skip-Gram and ngram2vec. The obtained results can serve as a baseline for future research on the Latvian language in NLP. The main conclusions are the following: First, in the part-of-speech task, using a training corpus 46 times smaller than in a previous study, the accuracy was 91.4 % (versus 98.3 % in the previous study). Second, fastText demonstrated the overall best effectiveness. Third, the best results for all methods were observed for embeddings with a dimension size of 200. Finally, word lemmatization generally did not improve results.

Evaluation of Word Embedding Models in Latvian NLP Tasks Based on Publicly Available Corpora

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

Nowadays, natural language processing (NLP) is increasingly relaying on pre-trained word embeddings for use in various tasks. However, there is little research...

Evaluation of Word Embedding Models in Latvian NLP Tasks Based on Publicly Available Corpora

Online veröffentlicht: 30. Dez. 2021

Seitenbereich: 132 - 138

DOI: https://doi.org/10.2478/acss-2021-0016

Schlüsselwörter
Named entity recognition, natural language processing, part-of-speech tagging, word analogy, word embeddings

© 2021 Rolands Laucis et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Evaluation of Word Embedding Models in Latvian NLP Tasks Based on Publicly Available Corpora

Online veröffentlicht: 30. Dez. 2021

Seitenbereich: 132 - 138

DOI: https://doi.org/10.2478/acss-2021-0016

SchlüsselwörterNamed entity recognition, natural language processing, part-of-speech tagging, word analogy, word embeddings

© 2021 Rolands Laucis et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Named entity recognition, natural language processing, part-of-speech tagging, word analogy, word embeddings