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Možnosti a meze tvorby tzv. optimálních referenčních překladů: po stopách "překladatelštiny" v profesionálních překladech zpravodajských textů

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Kloudová V., Mraček D., Bojar O., Popel M.
Slovo a slovesnost: časopis pro otázky teorie a kultury jazyka (Slovo a slovesnost: A journal for the theory of language and language cultivation)
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2023
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vol. 84
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issue 2
122-156
EN
The study explores translation quality by analysing two Czech professional translations of English newspaper articles. The original idea was for a tandem of translators-cum-theoreticians to synthesise the best of the two translations while introducing slight to moderate modifications where necessary, to produce an optimal reference translation, i.e., a translation thought to be the best possible that can be achieved by a team of human translators; optimal reference translations can be used in assessments of excellent machine translations. It soon became apparent, however, that a considerable amount of editing and creativity was needed from the team striving for an optimal reference translation, prompting the present authors to subject the original translations to a detailed assessment. The primary focus is on the formal aspect of the translations and the phenomenon known as ‘translationese’, which is understood here to refer to a lack of sensitivity to target language usage. The problems identified fall into a wide range of categories such as spelling, morphosyntax, grammar, lexicon and word formation. Special attention is paid to source-language interference; having reviewed existing theoretical discussions of interference, the authors drafted a typology which was then expanded to include several other types of errors recurrent in the translations analysed.
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Hluboké učení v automatické analýze českého textu

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Straková J., Straka M., Hajič J., Popel M.
Slovo a slovesnost: časopis pro otázky teorie a kultury jazyka (Slovo a slovesnost: A journal for the theory of language and language cultivation)
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2019
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vol. 80
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issue 4
306-327
EN
The deep learning methods of artificial neural networks have seen a significant uptake in recent years, and have succeeded in overcoming and advancing the success of auto-solving tasks in many fields. The field of computational linguistics and its application offshoot, natural language processing, with classic tasks such as morphological tagging, dependency analysis, named entity recognition and machine translation, are no exception to this. This paper provides an overview of recent advances in these tasks related to the Czech language and presents completely new results in the areas of morphological marking and recognition of named entities in Czech, along with a detailed error analysis.
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