References
- [1] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in Workshop Track Proceedings of 1st International Conference on Learning Representations, Scottsdale, Arizona, USA, May 2013, pp. 1–12.
- [2] P. Jeffrey, S. Richard, and D. M. Christopher, “GloVe: Global vectors for word representation,” in Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1532–1543.
- [3] B. Wang, A. Wang, F. Chen, Y. Wang, and C.-C. J. Kuo, “Evaluating word embedding models: methods and experimental results,”APSIPA Transactions on Signal and Information Processing, vol. 8, Art no. e19, Jul. 2019. https://doi.org/10.1017/ATSIP.2019.1210.1017/ATSIP.2019.12
- [4] A. Znotiņš, “Word embeddings for Latvian natural language processing tools,”Human Language Technologies – The Baltic Perspective, vol. 289, IOS Press, pp. 167–173, 2016. https://doi.org/10.3233/978-1-61499-701-6-167
- [5] A. Znotiņš and G. Barzdiņš, “LVBERT: Transformer-based model for Latvian language understanding,” Human Language Technologies – The Baltic Perspective, vol. 328, IOS Press, pp. 111–115, 2020. https://doi.org/10.3233/FAIA20061010.3233/FAIA200610
- [6] R. Vīksna and I. Skadiņa, “Large language models for Latvian named entity recognition,” Human Language Technologies – The Baltic Perspective, vol. 328,IOS Press, pp. 62–69, 2020. https://doi.org/10.3233/FAIA20060310.3233/FAIA200603
- [7] “EuroParl,” [Online]. Available: https://www.statmt.org/europarl/. Accessed on: May 2021.
- [8] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding”, in Proceedings of NAACL-HLT, Minneapolis, Minnesota, 2019, p. 4171–4186.
- [9] M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep contextualized word representations,” arXiv, Art no.1802.05365, pp. 1–15, 2018.
- [10] M. Ulčar, A. Žagar, C. Armendariz, A. Repar, S. Pollak, M. Purver, and M. Robnik-Šikonja, “Evaluation of contextual embeddings on less-resourced languages,” arXiv, Art no. 2107.10614, pp. 1–45, 2021.
- [11] X. Rong, “word2vec parameter learning explained”, arXiv, Art no. 1411.2738v4, pp. 1–21, 2016.
- [12] W. Ling, C. Dyer, A. Black, and I. Trancoso, “Two/Too simple adaptations of Word2Vec for syntax problems”, in Proceedings of the 2015 Conference of the North American, Denver, Colorado, May-June 2015, pp. 1299–1304. https://doi.org/10.3115/v1/N15-114210.3115/v1/N15-1142
- [13] P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, “Enriching word vectors with subword information”, Transactions of the Association for Computational Linguistics, vol. 5, June 2017, pp. 135–146. https://doi.org/10.1162/tacl_a_0005110.1162/tacl_a_00051
- [14] Z. Zhao, T. Liu, S. Li, and B. Li, “Ngram2vec: Learning improved word representations from Ngram co-occurrence statistics”, in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, Sep. 2017, pp. 244–253. https://doi.org/10.18653/v1/D17-102310.18653/v1/D17-1023
- [15] “UDPipe,” [Online]. Available: https://ufal.mff.cuni.cz/udpipe/1. Accessed on: May 2021.
- [16] “Gensim library,” [Online]. Available: https://radimrehurek.com/gensim/. Accessed on: May 2021.
- [17] “Ngram2vec tool repository,” [Online]. Available: https://github.com/zhezhaoa/ngram2vec. Accessed on: May 2021.
- [18] “Structured Skip-Gram tool repository,” [Online]. Available: https://github.com/wlin12/wang2vec. Accessed on: May 2021.
- [19] M. Ulčar, K. Vaik, J. Lindstrom, M. Dailidenaite, and M. Robnik-Sikonja, “Multilingual culture-independent word analogy datasets,” in Proceedings of the 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, May 2020, pp. 4074–4080.
- [20] “Translated analogy dataset repository,” [Online]. Available: https://www.clarin.si/repository/xmlui/handle/11356/1261. Accessed on: May 2021.
- [21] “SpaCy tool,” [Online]. Available: https://spacy.io/. Accessed on: May 2021.
- [22] “LVTB dataset repository.” [Online]. Available: https://github.com/UniversalDependencies/UD_Latvian-LVTB/tree/master. Accessed on: May 2021.
- [23] “LUMII_AiLab NER dataset repository.” [Online]. Available: https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities. Accessed on: May 2021.
- [24] O. Levy and Y. Goldberg, “Linguistic Regularities in Sparse and Explicit Word Representations”, in Proceedings of the Eighteenth Conference on Computational Natural Language Learning, June 2014, pp. 171–180. https://doi.org/10.3115/v1/W14-161810.3115/v1/W14-1618
- [25] “CommonCrawl,” [Online]. Available: https://commoncrawl.org/. Accessed on: May 2021.
- [26] P. Paikens, “Deep neural learning approaches for Latvian morphological tagging,” Human Language Technologies – The Baltic Perspective, vol. 289,IOS Press, pp. 160–166, 2016.https://doi.org/10.3233/978-1-61499-701-6-160