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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 Page: 1 2 3 4 5...10 Hits 1 – 20 of 191 1 Abstracts from the KAS corpus KAS-Abs 2.0 Žagar, Aleš; Kavaš, Matic; Robnik-Šikonja, Marko. - : Faculty of Electrical Engineering and Computer Science, University of Maribor, 2022. : Faculty of Computer and Information Science, University of Ljubljana, 2022 BASE Show details 2 The CLASSLA-StanfordNLP model for lemmatisation of standard Slovenian 1.4 Ljubešić, Nikola; Krsnik, Luka. - : Jožef Stefan Institute, 2022 BASE Show details 3 Corpus of academic Slovene KAS 2.0 Žagar, Aleš; Kavaš, Matic; Robnik-Šikonja, Marko. - : Faculty of Electrical Engineering and Computer Science, University of Maribor, 2022. : Faculty of Computer and Information Science, University of Ljubljana, 2022 BASE Show details 4 The Twitter user dataset for discriminating between Bosnian, Croatian, Montenegrin and Serbian Twitter-HBS 1.0 Ljubešić, Nikola; Rupnik, Peter. - : Jožef Stefan Institute, 2022 BASE Show details 5 Summarization datasets from the KAS corpus KAS-Sum 1.0 Žagar, Aleš; Kavaš, Matic; Robnik-Šikonja, Marko. - : Faculty of Electrical Engineering and Computer Science, University of Maribor, 2022. : Faculty of Computer and Information Science, University of Ljubljana, 2022 BASE Show details 6 Machine Translation datasets from the KAS corpus KAS-MT 1.0 Žagar, Aleš; Kavaš, Matic; Robnik-Šikonja, Marko. - : Faculty of Electrical Engineering and Computer Science, University of Maribor, 2022. : Faculty of Computer and Information Science, University of Ljubljana, 2022 BASE Show details 7 ASR training dataset for Croatian ParlaSpeech-HR v1.0 Ljubešić, Nikola; Koržinek, Danijel; Rupnik, Peter. - : Jožef Stefan Institute, 2022 BASE Show details 8 The news dataset for discriminating between Bosnian, Croatian and Serbian SETimes.HBS 1.0 Ljubešić, Nikola; Rupnik, Peter. - : Jožef Stefan Institute, 2022 BASE Show details 9 The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Slovenian 1.3 Ljubešić, Nikola; Krsnik, Luka. - : Jožef Stefan Institute, 2022 BASE Show details 10 The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild ... Kuzman, Taja; Rupnik, Peter; Ljubešić, Nikola. - : arXiv, 2022 BASE Show details 11 Geographic Adaptation of Pretrained Language Models ... Hofmann, Valentin; Glavaš, Goran; Ljubešić, Nikola. - : arXiv, 2022 BASE Show details 12 The ParlaMint corpora of parliamentary proceedings Erjavec, Tomaž; Ogrodniczuk, Maciej; Osenova, Petya. - 2022 BASE Show details 13 Retweet communities reveal the main sources of hate speech Evkoski, Bojan; Pelicon, Andraž; Mozetič, Igor... In: PLoS One (2022) BASE Show details 14 The ParlaMint corpora of parliamentary proceedings Erjavec, Tomaž; Ogrodniczuk, Maciej; Osenova, Petya... In: Lang Resour Eval (2022) BASE Show details 15 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 16 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 17 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 18 Choice of plausible alternatives dataset in Croatian COPA-HR Ljubešić, Nikola. - : Jožef Stefan Institute, 2021 BASE Show details 19 Croatian corpus of non-professional written language by typical speakers and speakers with language disorders RAPUT 1.0 Kuvač Kraljević, Jelena; Hržica, Gordana; Štefanec, Vanja. - : Jožef Stefan Institute, 2021. : Faculty of Education and Rehabilitation, University of Zagreb, 2021 BASE Show details 20 The Orange workflow for observing collocation trends ColTrend 1.0 Kosem, Iztok; Krek, Simon; Čibej, Jaka; Gantar, Polona; Arhar Holdt, Špela; Logar, Nataša; Laskowski, Cyprian; Klemenc, Bojan; Ljubešić, Nikola; Dobrovoljc, Kaja; Gorjanc, Vojko; Pori, Eva. - : Centre for Language Resources and Technologies, University of Ljubljana, 2021 Abstract: The Orange workflow for observing collocation trends ColTrend 1.0 ColTrend is a workflow (.OWS file) for Orange Data Mining (an open-source machine learning and data visualization software: https://orangedatamining.com/) that allows the user to observe temporal collocation trends in corpora. The workflow consists of a series of Python scripts, data filters, and visualizers. As input, the workflow takes a .CSV file with data on collocations and their relative frequencies by year of publication extracted from a corpus. As output, it provides a .TSV file containing the same data (or a filtered selection thereof) enriched with four measures that indicate the collocation’s temporal trend in the corpus: (1) the slope (k) of a linear regression model fitted to the frequency data, which indicates whether the frequency of use of the collocation is increasing or declining; (2) the coefficient of determination (R2) of the linear regression model, indicating how linear the change in the collocation’s use is; (3) the ratio (m) of maximum relative frequency and average relative frequency, which indicates peaks in collocation usage; and (4) the coefficient of recent growth (t), which indicates an increased usage of the collocation in the last three years of the observed corpus data. The entry also contains three .CSV files that can be used to test the workflow. The files contain collocation candidates (along with their relative frequencies per year of publication) extracted from the Gigafida 2.0 Corpus of Written Slovene (https://viri.cjvt.si/gigafida/) with three different syntactic structures (as defined in http://hdl.handle.net/11356/1415): 1) p0-s0 (adjective + noun, e.g. rezervni sklad), 2) s0-s2 (noun + noun in the genitive case, e.g. ukinitev lastnine), and 3) gg-s4 (verb + noun in the accusative case, e.g. pripraviti besedilo). It should be noted that only collocation candidates with absolute frequency of 15 and above were extracted. Please note that the ColTrend workflow requires the installation of the Text Mining add-on for Orange. For installation instructions as well as a more detailed description of the different phases of the workflow and the measures used to observe the collocation trends, please consult the README file. Keyword: collocations; linear regression; relative frequency; temporal trends URL: http://hdl.handle.net/11356/1424 BASE Hide details Page: 1 2 3 4 5...10

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