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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 Hits 1 – 20 of 57 1 The CLASSLA-StanfordNLP model for lemmatisation of standard Slovenian 1.4 Ljubešić, Nikola; Krsnik, Luka. - : Jožef Stefan Institute, 2022 BASE Show details 2 The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Slovenian 1.3 Ljubešić, Nikola; Krsnik, Luka. - : Jožef Stefan Institute, 2022 BASE Show details 3 Geographic Adaptation of Pretrained Language Models ... Hofmann, Valentin; Glavaš, Goran; Ljubešić, Nikola. - : arXiv, 2022 BASE Show details 4 The Orange workflow for observing collocation trends ColTrend 1.0 Kosem, Iztok; Krek, Simon; Čibej, Jaka. - : Centre for Language Resources and Technologies, University of Ljubljana, 2021 BASE Show details 5 The Orange workflow for observing collocation clusters ColEmbed 1.0 Kosem, Iztok; Čibej, Jaka; Ljubešić, Nikola; Krek, Simon; Gantar, Polona; Arhar Holdt, Špela; Logar, Nataša; Laskowski, Cyprian; Klemenc, Bojan; Dobrovoljc, Kaja; Gorjanc, Vojko; Pori, Eva. - : Centre for Language Resources and Technologies, University of Ljubljana, 2021 Abstract: The Orange Workflow for Observing Collocation Clusters ColEmbed 1.0 ColEmbed 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 clusters of collocation candidates extracted from corpora. The workflow consists of a series of data filters, embedding processors, and visualizers. As input, the workflow takes a tab-separated file (.TSV/.TAB) with data on collocations extracted from a corpus, along with their relative frequencies by year of publication and other optional values (such as information on temporal trends). The workflow allows the user to select the features which are then used in the workflow to cluster collocation candidates, along with the embeddings generated based on the selected lemmas (either one lemma or both lemmas can be selected, depending on our clustering criteria; for instance, if we wish to cluster adjective+noun candidates based on the similarities of their noun components, we only select the second lemma to be taken into account in embedding generation). The obtained embedding clusters can be visualized and further processed (e.g. by finding the closest neighbors of a reference collocation). The workflow is described in more detail in the accompanying README file. The entry also contains three .TAB files that can be used to test the workflow. The files contain collocation candidates (along with their relative frequencies per year of publication and four measures describing their temporal trends; see http://hdl.handle.net/11356/1424 for more details) 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 ColEmbed 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: clustering; collocations; temporal trends; word embeddings URL: http://hdl.handle.net/11356/1425 BASE Hide details 6 The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Slovenian 1.2 Ljubešić, Nikola; Krsnik, Luka. - : Jožef Stefan Institute, 2021 BASE Show details 7 The CLASSLA-StanfordNLP model for lemmatisation of standard Slovenian 1.3 Ljubešić, Nikola; Krsnik, Luka. - : Jožef Stefan Institute, 2021 BASE Show details 8 The CLASSLA-StanfordNLP model for lemmatisation of standard Macedonian 1.0 Ljubešić, Nikola; Zdravkova, Katerina; Erjavec, Tomaž. - : Jožef Stefan Institute, 2020 BASE Show details 9 The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Macedonian 1.0 Ljubešić, Nikola; Zdravkova, Katerina; Stojanoska, Sanja. - : Jožef Stefan Institute, 2020 BASE Show details 10 The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Serbian 1.1 Ljubešić, Nikola. - : Jožef Stefan Institute, 2020 BASE Show details 11 The CLASSLA-StanfordNLP model for JOS dependency parsing of standard Slovenian 1.0 Ljubešić, Nikola. - : Jožef Stefan Institute, 2020 BASE Show details 12 The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Croatian 1.1 Ljubešić, Nikola. - : Jožef Stefan Institute, 2020 BASE Show details 13 Word embeddings CLARIN.SI-embed.mk 0.1 Ljubešić, Nikola. - : Jožef Stefan Institute, 2020 BASE Show details 14 The CLASSLA-StanfordNLP model for named entity recognition of standard Slovenian 1.0 Ljubešić, Nikola. - : Jožef Stefan Institute, 2020 BASE Show details 15 The CLASSLA-StanfordNLP model for named entity recognition of non-standard Croatian 1.0 Ljubešić, Nikola. - : Jožef Stefan Institute, 2020 BASE Show details 16 The CLASSLA-StanfordNLP model for lemmatisation of standard Slovenian 1.1 Ljubešić, Nikola. - : Jožef Stefan Institute, 2020 BASE Show details 17 The CLASSLA-StanfordNLP model for named entity recognition of standard Croatian 1.0 Ljubešić, Nikola. - : Jožef Stefan Institute, 2020 BASE Show details 18 The CLASSLA-StanfordNLP model for lemmatisation of standard Serbian 1.2 Ljubešić, Nikola. - : Jožef Stefan Institute, 2020 BASE Show details 19 The CLASSLA-StanfordNLP model for lemmatisation of standard Serbian 1.1 Ljubešić, Nikola. - : Jožef Stefan Institute, 2020 BASE Show details 20 The CLASSLA-StanfordNLP model for lemmatisation of non-standard Serbian 1.1 Ljubešić, Nikola; Štefanec, Vanja. - : Jožef Stefan Institute, 2020 BASE Show details Page: 1 2 3

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