Migrant Stories is a corpus of 1017 short biographic narratives of migrants supplemented with meta information about countries of origin/destination, the migrant gender, GDP per capita of the respective countries, etc. The corpus has been compiled as a teaching material for data analysis.
Czech morphological dictionary developed originally by Jan Hajič as a spelling checker and lemmatization dictionary. Currently it contains full morphological information for each covered wordform, as well as some derivational, semantic and named entity information.
MorfFlex CZ 2.0 is the Czech morphological dictionary developed originally by Jan Hajič as a spelling checker and lemmatization dictionary. MorfFlex is a flat list of lemma-tag-wordform triples. For each wordform, full inflectional information is coded in a positional tag. Wordforms are organized into entries (paradigm instances or paradigms in short) according to their formal morphological behavior. The paradigm (set of wordforms) is identified by a unique lemma. Apart from traditional morphological categories, the description also contains some semantic, stylistic and derivational information. For more details see a comprehensive specification of the Czech morphological annotation http://ufal.mff.cuni.cz/techrep/tr64.pdf .
Slovak morphological dictionary modeled after the Czech one. It consists of (word form, lemma, POS tag) triples, reusing the Czech morphological system for POS tags and lemma descriptions.
MUSCIMA++ is a dataset of handwritten music notation for musical symbol detection. It contains 91255 symbols, consisting of both notation primitives and higher-level notation objects, such as key signatures or time signatures. There are 23352 notes in the dataset, of which 21356 have a full notehead, 1648 have an empty notehead, and 348 are grace notes. For each annotated object in an image, we provide both the bounding box, and a pixel mask that defines exactly which pixels within the bounding box belong to the given object. Composite constructions, such as notes, are captured through explicitly annotated relationships of the notation primitives (noteheads, stems, beams...). This way, the annotation provides an explicit bridge between the low-level and high-level symbols described in Optical Music Recognition literature.
MUSCIMA++ has annotations for 140 images from the CVC-MUSCIMA dataset [2], used for handwritten music notation writer identification and staff removal. CVC-MUSCIMA consists of 1000 binary images: 20 pages of music were each re-written by 50 musicians, binarized, and staves were removed. We had 7 different annotators marking musical symbols: each annotator marked one of each 20 CVC-MUSCIMA pages, with the writers selected so that the 140 images cover 2-3 images from each of the 50 CVC-MUSCIMA writers. This setup ensures maximal variability of handwriting, given the limitations in annotation resources.
The MUSCIMA++ dataset is intended for musical symbol detection and classification, and for music notation reconstruction. A thorough description of its design is published on arXiv [2]: https://arxiv.org/abs/1703.04824 The full definition of the ground truth is given in the form of annotator instructions.
NER models for NameTag 2, named entity recognition tool, for English, German, Dutch, Spanish and Czech. Model documentation including performance can be found here: https://ufal.mff.cuni.cz/nametag/2/models . These models are for NameTag 2, named entity recognition tool, which can be found here: https://ufal.mff.cuni.cz/nametag/2 .
NER models for NameTag 2, named entity recognition tool, for English, German, Dutch, Spanish and Czech. Model documentation including performance can be found here: https://ufal.mff.cuni.cz/nametag/2/models . These models are for NameTag 2, named entity recognition tool, which can be found here: https://ufal.mff.cuni.cz/nametag/2 .
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.
This is a trained model for the supervised machine learning tool NameTag 3 (https://ufal.mff.cuni.cz/nametag/3/), trained jointly on several NE corpora: English CoNLL-2003, German CoNLL-2003, Dutch CoNLL-2002, Spanish CoNLL-2002, Ukrainian Lang-uk, and Czech CNEC 2.0, all harmonized to flat NEs with 4 labels PER, ORG, LOC, and MISC. 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#multilingual-conll.
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.