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The CLASSLA-StanfordNLP model for lemmatisation of standard Slovenian 1.4
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The Twitter user dataset for discriminating between Bosnian, Croatian, Montenegrin and Serbian Twitter-HBS 1.0
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The news dataset for discriminating between Bosnian, Croatian and Serbian SETimes.HBS 1.0
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The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Slovenian 1.3
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The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild ...
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Abstract:
This paper presents a new training dataset for automatic genre identification GINCO, which is based on 1,125 crawled Slovenian web documents that consist of 650 thousand words. Each document was manually annotated for genre with a new annotation schema that builds upon existing schemata, having primarily clarity of labels and inter-annotator agreement in mind. The dataset consists of various challenges related to web-based data, such as machine translated content, encoding errors, multiple contents presented in one document etc., enabling evaluation of classifiers in realistic conditions. The initial machine learning experiments on the dataset show that (1) pre-Transformer models are drastically less able to model the phenomena, with macro F1 metrics ranging around 0.22, while Transformer-based models achieve scores of around 0.58, and (2) multilingual Transformer models work as well on the task as the monolingual models that were previously proven to be superior to multilingual models on standard NLP tasks. ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2201.03857 https://arxiv.org/abs/2201.03857
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Retweet communities reveal the main sources of hate speech
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In: PLoS One (2022)
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The ParlaMint corpora of parliamentary proceedings
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In: Lang Resour Eval (2022)
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Choice of plausible alternatives dataset in Croatian COPA-HR
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Croatian corpus of non-professional written language by typical speakers and speakers with language disorders RAPUT 1.0
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The Orange workflow for observing collocation trends ColTrend 1.0
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