[
[1] Hays, D. G. (1964), Dependency Theory: A Formalism and Some Observations, Language, vol. 40, p. 511, 10.
]Search in Google Scholar
[
[2] Marcheggiani, D. and Titov, I. (2017), Encoding sentences with graph convolutional networks for semantic role labeling, Proceedings of the Conference on Empirical Methods for Natural Language Processing.10.18653/v1/D17-1159
]Search in Google Scholar
[
[3] Zhang, Y., Qi, P. and Manning, C. D. (2018), Graph convolution over pruned dependency trees improves relation extraction, Proceedings of the Conference on Empirical Methods for Natural Language Processing.10.18653/v1/D18-1244
]Search in Google Scholar
[
[4] Qi, P., Dozat, T., Zhang, Y. and Manning, C. D. (2019), Universal Dependency Parsing from Scratch, arXiv:1901.10457.
]Search in Google Scholar
[
[5] Choi, J. D. and Palmer, M. (2011), Getting the most out of transition-based dependency parsing.
]Search in Google Scholar
[
[6] Nivre, J. (2009), Non-projective dependency parsing in expected linear time, Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL, p. pp. 351–359.
]Search in Google Scholar
[
[7] McDonald, R., Pereira, F., Ribarov, K. and Hajič, J. (2005), Non-projective dependency parsing using spanning tree algorithms, Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, p. 523–530.
]Search in Google Scholar
[
[8] Kanerva, J., Ginter, F., and Salakoski, T. (2020), Universal Lemmatizer: A sequence-to-sequence model for lemmatizing Universal Dependencies treebanks, Natural Language Engineering, pp. 1-30.
]Search in Google Scholar
[
[9] Straka, M., and Straková, J. (2017), Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe, in Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies.10.18653/v1/K17-3009
]Search in Google Scholar
[
[10] Mikolov, T., Chen, K., Corrado, G. and Dean, J. (2014), Word2Vec, [Online]. Available: https://code.google.com/p/word2vec/
]Search in Google Scholar
[
[11] Devlin, J., Chang, M.-W., Lee, K. and Toutanova, K. (2019), BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis.
]Search in Google Scholar
[
[12] Deeplearning4j: Open source distributed deep learning for the JVM (2017), Apache Software Foundation License 2.0., [Online]. Available: http://deeplearning4j.org/
]Search in Google Scholar
[
[13] Nivre, J., Hall, J., Kübler, S., McDonald, R., Nilsson, J., Riedel, S. and Yuret, D. (2007), The CoNLL 2007 Shared Task on Dependency Parsing, in Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Prague.
]Search in Google Scholar
[
[14] Nivre, J., de Marneffe, M.-C., Ginter, F., Goldberg, Y., Hajič, J., Manning, C. D. McDonald, R., Petrov, S., Pyysalo, S., Silveira, N., Tsarfaty, R. and Zeman, D. (2016), Universal Dependencies v1: A Multilingual Treebank Collection, in Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), Portorož, Slovenia.10.1162/coli_a_00402
]Search in Google Scholar
[
[15] Vincze, V., Simkó, K., Szántó, Z. and Farkas, R. (2017), Universal Dependencies and Morphology for Hungarian - and on the Price of Universality, in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers.10.18653/v1/E17-1034
]Search in Google Scholar
[
[16] Vincze, V., Szauter, D., Almási, A., Móra, G. Alexin, Z. and Csirik, J. (2010), Hungarian Dependency Treebank, in Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), Valletta.
]Search in Google Scholar
[
[17] Nivre, J. (2008), Algorithms for Deterministic Incremental Dependency Parsing, Computational Linguistics, vol. 34, p. 513–553, 12.
]Search in Google Scholar
[
[18] Sartorio, F. (2015), Improvements in Transition Based Systems for Dependency Parsing, PhD Thesis work.
]Search in Google Scholar
[
[19] Noji, H. and Miyao, Y. (2015), Left-corner Parsing for Dependency Grammar, Journal of Natural Language Processing, p. 251–288.
]Search in Google Scholar
[
[20] Gómez-Rodríguez, C., Shi, T. and Lee, L. (2018), Global Transition-based Non-projective Dependency Parsing, in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).10.18653/v1/P18-1248
]Search in Google Scholar
[
[21] Tálas, D. and Novák, A. (2019), Különböző függőségi elemzők teljesítményének vizsgálata magyar nyelven (Examining the performance of different dependency parsers for Hungarian language), Conference of Hungarian Computational Linguistics, p. 345–354.
]Search in Google Scholar
[
[22] Kovács, L. and Csépányi-Fürjes, L. (2020), Feature reduction for dependency graph construction in computational linguistics, in CEUR Workshop Proceedings.
]Search in Google Scholar
[
[23] Nivre, J. and Hall, J. (2018), MaltParser, [Online], Available: http://www.maltparser.org/
]Search in Google Scholar
[
[24] Chen, D. and Manning, C. D. (2014), A Fast and Accurate Dependency Parser using Neural Networks, in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).10.3115/v1/D14-1082
]Search in Google Scholar
