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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
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2
The CLASSLA-StanfordNLP model for lemmatisation of standard Slovenian 1.4
Ljubešić, Nikola; Krsnik, Luka. - : Jožef Stefan Institute, 2022
BASE
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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
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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
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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
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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
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7
ASR training dataset for Croatian ParlaSpeech-HR v1.0
Ljubešić, Nikola; Koržinek, Danijel; Rupnik, Peter. - : Jožef Stefan Institute, 2022
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8
The news dataset for discriminating between Bosnian, Croatian and Serbian SETimes.HBS 1.0
Ljubešić, Nikola; Rupnik, Peter. - : Jožef Stefan Institute, 2022
Abstract: The SETimes.HBS dataset consists of parallel documents written in Bosnian, Croatian and Serbian, harvested from the already inactive setimes.com website publishing news in the languages of South-Eastern Europe. While the writing process of the documents is not known, they are quite likely independent translations from English. The main intended usage of this dataset is closely-related language discrimination. This dataset is not a traditional parallel dataset as there are no explicit links between parallel documents. Special care was taken that the training, development and testing bins of the dataset contain the same documents in all three languages as data leakage between the three bins, given the similarity of the three languages, could be problematic for benchmarking.
Keyword: closely related languages; language identification; news corpus
URL: http://hdl.handle.net/11356/1461
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9
The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Slovenian 1.3
Ljubešić, Nikola; Krsnik, Luka. - : Jožef Stefan Institute, 2022
BASE
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10
The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild ...
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11
Geographic Adaptation of Pretrained Language Models ...
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12
The ParlaMint corpora of parliamentary proceedings
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13
Retweet communities reveal the main sources of hate speech
In: PLoS One (2022)
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14
The ParlaMint corpora of parliamentary proceedings
In: Lang Resour Eval (2022)
BASE
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15
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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16
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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17
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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18
Choice of plausible alternatives dataset in Croatian COPA-HR
Ljubešić, Nikola. - : Jožef Stefan Institute, 2021
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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
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20
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
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