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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 14 of 14 1 Indonesian web corpus MEDVEĎ, MAREK; Suchomel, Vít. - : Masaryk University, NLP Centre, 2019 BASE Show details 2 SkELL corpora as a part of the language portal Sõnaveeb: problems and perspectives ... Koppel, Kristina; Kallas, Jelena; Khokhlova, Maria; Suchomel, Vít; Baisa, Vít; Michelfeit, Jan. - : Zenodo, 2019 Abstract: In this paper we analyse the quality and presentation of authentic corpus sentences from Sketch Engine for Language Learning (SkELL) corpora (Baisa & Suchomel 2014), based on the example of Sõnaveeb (Wordweb), a new language portal being developed in the Institute of the Estonian Language. Currently Sõnaveeb contains a total of 150,000 Estonian headwords; about 70,000 of them have Russian equivalents. Authentic corpus sentences are displayed for both languages. In some cases (e.g. terms, derived forms, compounds and multi-word expressions), corpus sentences are the only source of usage examples that are available on the portal. This paper describes parameters of Good Dictionary Examples (GDEX) (Kilgarriff et al., 2008) configurations for Estonian (GDEX 1.4.) and for Russian (GDEX 1.2) used for the compilation of etSkELL 2018 and ruSkELL 1.6 corpora, gives an overview of an evaluation of the GDEX 1.4. configuration for Estonian, and outlines the requirements for user-friendly SkELL corpora presentation as ... Keyword: Estonian; GDEX; Good Dictionary Example; learner corpus; lexicographic standards objective 2; Russian; SkELL; strategies, tools, standards for lexicographic resources objective 3; WP4 URL: https://zenodo.org/record/3612932 https://dx.doi.org/10.5281/zenodo.3612932 BASE Hide details 3 Automating Dictionary Production: a Tagalog-English-Korean Dictionary from Scratch ... Baisa, Vít; Blahuš, Marek; Cukr, Michal. - : Zenodo, 2019 BASE Show details 4 Automating Dictionary Production: a Tagalog-English-Korean Dictionary from Scratch ... Baisa, Vít; Blahuš, Marek; Cukr, Michal. - : Zenodo, 2019 BASE Show details 5 SkELL corpora as a part of the language portal Sõnaveeb: problems and perspectives ... Koppel, Kristina; Kallas, Jelena; Khokhlova, Maria. - : Zenodo, 2019 BASE Show details 6 Somali Web Corpus Suchomel, Vít; Rychlý, Pavel. - : Masaryk University, NLP Centre, 2018 BASE Show details 7 Oromo web corpus Suchomel, Vít; Rychlý, Pavel. - : Masaryk University, NLP Centre, 2018 BASE Show details 8 Amharic Web Corpus Suchomel, Vít; Rychlý, Pavel. - : Masaryk University, NLP Centre, 2018 BASE Show details 9 Tigrinya Web Corpus Suchomel, Vít; Rychlý, Pavel. - : Masaryk University, NLP Centre, 2018 BASE Show details 10 Indonesian web corpus (idWac) Medveď, Marek; Suchomel, Vít. - : Natural Language Processing Centre, Faculty of Informatics, Masaryk University, 2018 BASE Show details 11 Removing spam from web corpora through supervised learning using FastText Suchomel, Vít [Verfasser]; Bański, Piotr [Herausgeber]; Kupietz, Marc [Herausgeber]. - Mannheim : Institut für Deutsche Sprache, Bibliothek, 2017 DNB Subject Category Language Show details 12 The Sketch Engine: ten years on Kilgarriff, Adam; Baisa, Vít; Bušta, Jan... In: Lexicography. Journal of ASIALEX 1 (2014) 1, 7-36 IDS OBELEX meta Show details 13 HindMonoCorp 0.5 Bojar, Ondřej; Diatka, Vojtěch; Rychlý, Pavel. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2014 BASE Show details 14 Removing spam from web corpora through supervised learning using FastText [Online resource] Suchomel, Vít IDS-Repository Show details

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