BibTeX

@inProceedings{adesam-berdicevskis-2021-part-304973,
	title        = {Part-of-speech tagging of Swedish texts in the neural era},
	booktitle    = {Proceedings of the 23rd Nordic Conference on Computational Linguistics, NoDaLiDa, May 31–2 June, 2021, Reykjavik, Iceland (online) / eds Simon Dobnik and Lilja Øvrelid},
	author       = {Adesam, Yvonne and Berdicevskis, Aleksandrs},
	year         = {2021},
	publisher    = { Linköping University Electronic Press},
	address      = {Linköping},
	ISBN         = { 978-91-7929-614-8},
	pages        = {200--209},
}

@inProceedings{berdicevskis-2021-successes-311655,
	title        = {Successes and failures of Menzerath’s law at the syntactic level},
	booktitle    = {Proceedings of the Second Workshop on Quantitative Syntax (Quasy, SyntaxFest 2021), 21–25 March, 2022, Sofia, Bulgaria / Radek Čech, Xinying Chen (eds.)},
	author       = {Berdicevskis, Aleksandrs},
	year         = {2021},
	publisher    = {Association for Computational Linguistics},
	address      = {Stroudsburg, PA},
	ISBN         = { 978-1-955917-15-5},
	pages        = {17--33},
}

@article{basirat-etal-2021-empirical-302492,
	title        = {An empirical study on the contribution of formal and semantic features to the grammatical gender of nouns},
	abstract     = {This study conducts an experimental evaluation of two hypotheses about the contributions of formal and semantic features to the grammatical gender assignment of nouns. One of the hypotheses (Corbett and Fraser 2000) claims that semantic features dominate formal ones. The other hypothesis, formulated within the optimal gender assignment theory (Rice 2006), states that form and semantics contribute equally. Both hypotheses claim that the combination of formal and semantic features yields the most accurate gender identification. In this paper, we operationalize and test these hypotheses by trying to predict grammatical gender using only character-based embeddings (that capture only formal features), only context-based embeddings (that capture only semantic features) and the combination of both. We performed the experiment using data from three languages with different gender systems (French, German and Russian). Formal features are a significantly better predictor of gender than semantic ones, and the difference in prediction accuracy is very large. Overall, formal features are also significantly better than the combination of form and semantics, but the difference is very small and the results for this comparison are not entirely consistent across languages.},
	journal      = {Linguistics Vanguard},
	author       = {Basirat, Ali and Allassonnière-Tang, Marc and Berdicevskis, Aleksandrs},
	year         = {2021},
	volume       = {7},
	number       = {1},
}

@article{ehret-etal-2021-meaning-304914,
	title        = {Meaning and Measures: Interpreting and Evaluating Complexity Metrics},
	journal      = {Frontiers in communication},
	author       = {Ehret, Katharina and Blumenthal-Dramé, Alice and Bentz, Christian and Berdicevskis, Aleksandrs},
	year         = {2021},
	volume       = {6},
}

@edited_book{berdicevskis-piperski-2021-skljanki-311612,
	title        = {Tri skljanki popoludni i drugie zadachi po lingvistike},
	editor       = {Berdicevskis, Aleksandrs and Piperski, Alexander},
	year         = {2021},
	publisher    = {Alpina Non-Fiction},
	address      = {Moskva},
	ISBN         = {978-5-00139-130-2},
}