This is a trained model for the supervised machine learning tool NameTag 3 (https://ufal.mff.cuni.cz/nametag/3/), trained on the Czech Named Entity Corpus 2.0 (https://ufal.mff.cuni.cz/cnec/cnec2.0). NameTag 3 is an open-source tool for both flat and nested named entity recognition (NER). NameTag 3 identifies proper names in text and classifies them into a set of predefined categories, such as names of persons, locations, organizations, etc. The model documentation can be found at https://ufal.mff.cuni.cz/nametag/3/models#czech-cnec2.
NomVallex 2.0 is a manually annotated valency lexicon of Czech nouns and adjectives, created in the theoretical framework of the Functional Generative Description and based on corpus data (the SYN series of corpora from the Czech National Corpus and the Araneum Bohemicum Maximum corpus). In total, NomVallex is comprised of 1027 lexical units contained in 570 lexemes, covering the following parts-of-speech and derivational categories: deverbal or deadjectival nouns, and deverbal, denominal, deadjectival or primary adjectives. Valency properties of a lexical unit are captured in a valency frame (modeled as a sequence of valency slots, each supplemented with a list of morphemic forms) and documented by corpus examples. In order to make it possible to study the relationship between valency behavior of base words and their derivatives, lexical units of nouns and adjectives in NomVallex are linked to their respective base lexical units (contained either in NomVallex itself or, in case of verbs, in the VALLEX lexicon), linking up to three parts-of-speech (i.e., noun – verb, adjective – verb, noun – adjective, and noun – adjective – verb).
In order to facilitate comparison, this submission also contains abbreviated entries of the base verbs of these nouns and adjectives from the VALLEX lexicon and simplified entries of the covered nouns and adjectives from the PDT-Vallex lexicon.
The NomVallex I. lexicon describes valency of Czech deverbal nouns belonging to three semantic classes, i.e. Communication (dotaz 'question'), Mental Action (plán 'plan') and Psych State (nenávist 'hatred'). It covers both stem-nominals and root-nominals (dotazování se 'asking' and dotaz 'question'). In total, the lexicon includes 505 lexical units in 248 lexemes. Valency properties are captured in the form of valency frames, specifying valency slots and their morphemic forms, and are exemplified by corpus examples.
In order to facilitate comparison, this submission also contains abbreviated entries of the source verbs of these nouns from the Vallex lexicon and simplified entries of the covered nouns from the PDT-Vallex lexicon.
ORTOFON v1 is designed as a representation of authentic spoken Czech used in informal situations (private environment, spontaneity, unpreparedness etc.) in the area of the whole Czech Republic. The corpus is composed of 332 recordings from 2012–2017 and contains 1 014 786 orthographic words (i.e. a total of 1 236 508 tokens including punctuation); a total of 624 different speakers appear in the probes. ORTOFON v1 is fully balanced regarding the basic sociolinguistic speaker categories (gender, age group, level of education and region of childhood residence).
The transcription is linked to the corresponding audio track. Unlike the ORAL-series corpora, the transcription was carried out on two main tiers, orthographic and phonetic, supplemented by an additional metalanguage tier. ORTOFON v1 is lemmatized and morphologically tagged. The (anonymized) corpus is provided in a (semi-XML) vertical format used as an input to the Manatee query engine. The data thus correspond to the corpus available via the KonText query engine to registered users of the CNC at http://www.korpus.cz
Please note: this item includes only the transcriptions, audio (and the transcripts in their original format) is available under more restrictive non-CC license at http://hdl.handle.net/11234/1-2579
The contribution includes the data frames and the R script (Markdown file) belonging to the paper "Morphological and Pragmatic Conditioning of Reflexivity in Possessive Pronouns: Effects of Number and Form of Address in Czech" submitted to the journal Language Variation and Change in September 2024.
Experimental materials, data and R scripts used in the paper "Garden-path sentences and the diversity of their
(mis)representations" (Ceháková - Chromý, 2023).
RobeCzech is a monolingual RoBERTa language representation model trained on Czech data. RoBERTa is a robustly optimized Transformer-based pretraining approach. We show that RobeCzech considerably outperforms equally-sized multilingual and Czech-trained contextualized language representation models, surpasses current state of the art in all five evaluated NLP tasks and reaches state-of-theart results in four of them. The RobeCzech model is released publicly at https://hdl.handle.net/11234/1-3691 and https://huggingface.co/ufal/robeczech-base, both for PyTorch and TensorFlow.
The item contains a list of 2,058 noun/verb conversion pairs along with related formations (word-formation paradigms) provided with linguistic features, including semantic categories that characterize semantic relations between the noun and the verb in each conversion pair. Semantic categories were assigned manually by two human annotators based on a set of sentences containing the noun and the verb from individual conversion pairs. In addition to the list of paradigms, the item contains a set of 739 files (a separate file for each conversion pair) annotated by the annotators in parallel and a set of 2,058 files containing the final annotation, which is included in the list of paradigms.
Simple question answering database version 2.1 (SQAD_v2.1) created from Czech Wikipedia. Each record of SQAD consist of four files (in vertical form provided with lemmatization and POS tagging) and two metadata files.
Simple question answering database version 3 (SQAD v3) created from Czech Wikipedia. New version consits of 13477 records. Each record of SQAD consist of multiple files - question, answer extraction, answer selection, ulr, question metadata and in some cases answer context.