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.
MUSCIMarker is an open-source tool for annotating visual objects and their relationships in binary images. It is implemented in Python, known to run on Windows, Linux and OS X, and supports working offline. MUSCIMarker is being used for creating a dataset of musical notation symbols, but can support any object set.
The user documentation online is currently (12.2016) incomplete, as it is continually changing to reflect annotators' comments and incorporate new features. This version of the software is *not* the final one, and it is under continuous development (we're currently working on adding grayscale image support with auto-binarization, and Android support for touch-based annotation). However, the current version (1.1) has already been used to annotate more than 100 pages of sheet music, over all the major desktop OSes, and I believe it is already in a state where it can be useful beyond my immediate music notation data gathering use case.
Normalized Arabic Fragments for Inestimable Stemming (NAFIS) is an Arabic stemming gold standard corpus composed by a collection of texts, selected to be representative of Arabic stemming tasks and manually annotated.
NameTag 2 is a named entity recognition tool. It recognizes named entities (e.g., names, locations, etc.) and can recognize both flat and embedded (nested) entities. NameTag 2 can be used either as a commandline tool or by requesting the NameTag webservice.
NameTag webservice can be found at:
https://lindat.mff.cuni.cz/services/nametag/
NameTag commandline tool can be downloaded from NameTag GitHub repository, branch nametag2:
git clone https://github.com/ufal/nametag -b nametag2
Latest models and documentation can be found at:
https://ufal.mff.cuni.cz/nametag/2
This software subject to the terms of the Mozilla Public License, v. 2.0 (http://mozilla.org/MPL/2.0/). The associated models are distributed under CC BY-NC-SA license.
Please cite as:
Jana Straková, Milan Straka, Jan Hajič (2019): Neural Architectures for Nested NER through Linearization. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5326-5331, Association for Computational Linguistics, Stroudsburg, PA, USA, ISBN 978-1-950737-48-2 (https://aclweb.org/anthology/papers/P/P19/P19-1527/)
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.
Trailer for Eva tropí hlouposti (Eva Fools Around, dir. Martin Frič, 1939). Actresses Nataša Gollová and Adina Mandlová in Šťastnou cestu (Happy Journey, dir. Otakar Vávra, 1943).