Texts in 107 languages from the W2C corpus (http://hdl.handle.net/11858/00-097C-0000-0022-6133-9), first 1,000,000 tokens per language, tagged by the delexicalized tagger described in Yu et al. (2016, LREC, Portorož, Slovenia).
Changes in version 1.1:
1. Universal Dependencies tagset instead of the older and smaller Google Universal POS tagset.
2. SVM classifier trained on Universal Dependencies 1.2 instead of HamleDT 2.0.
3. Balto-Slavic languages, Germanic languages and Romance languages were tagged by classifier trained only on the respective group of languages. Other languages were tagged by a classifier trained on all available languages. The "c7" combination from version 1.0 is no longer used.
Sentence-parallel corpus made from English and Czech Wikipedias based on translated articles from English into Czech.
The work done is described in the paper: ŠTROMAJEROVÁ, Adéla, Vít BAISA a Marek BLAHUŠ. Between Comparable and Parallel: English-Czech Corpus from Wikipedia. In RASLAN 2016 Recent Advances in Slavonic Natural Language Processing. Brno: Tribun EU, 2016. s. 3-8, 6 s. ISBN 978-80-263-1095-2.
We have created test set for syntactic questions presented in the paper [1] which is more general than Mikolov's [2]. Since we were interested in morphosyntactic relations, we extended only the questions of the syntactic type with exception of nationality adjectives which is already covered completely in Mikolov's test set.
We constructed the pairs more or less manually, taking inspiration in the Czech side of the CzEng corpus [3], where explicit morphological annotation allows to identify various pairs of Czech words (different grades of adjectives, words and their negations, etc.). The word-aligned English words often shared the same properties. Another sources of pairs were acquired from various webpages usually written for learners of English. For example for verb tense, we relied on a freely available list of English verbs and their morphological variations.
We have included 100-1000 different pairs for each question set. The questions were constructed from the pairs similarly as by Mikolov: generating all possible pairs of pairs. This leads to millions of questions, so we randomly selected 1000 instances per question set, to keep the test set in the same order of magnitude. Additionally, we decided to extend set of questions on opposites to cover not only opposites of adjectives but also of nouns and verbs.
This package contains the eye-tracker recordings of 8 subjects evaluating English-to-Czech machine translation quality using the WMT-style ranking of sentences.
We provide the set of sentences evaluated, the exact screens presented to the annotators (including bounding box information for every area of interest and even for individual letters in the text) and finally the raw EyeLink II files with gaze trajectories.
The description of the experiment can be found in the paper:
Ondřej Bojar, Filip Děchtěrenko, Maria Zelenina. A Pilot Eye-Tracking Study of WMT-Style Ranking Evaluation.
Proceedings of the LREC 2016 Workshop “Translation Evaluation – From Fragmented Tools
and Data Sets to an Integrated Ecosystem”, Georg Rehm, Aljoscha Burchardt et al. (eds.). pp. 20-26. May 2016, Portorož, Slovenia.
This work has received funding from the European Union's Horizon 2020 research
and innovation programme under grant agreement no. 645452 (QT21). This work was
partially financially supported by the Government of Russian Federation, Grant
074-U01.
This work has been using language resources developed, stored and distributed
by the LINDAT/CLARIN project of the Ministry of Education, Youth and Sports of
the Czech Republic (project LM2010013).
Grammar Error Correction Corpus for Czech (GECCC) consists of 83 058 sentences and covers four diverse domains, including essays written by native students, informal website texts, essays written by Romani ethnic minority children and teenagers and essays written by nonnative speakers. All domains are professionally annotated for GEC errors in a unified manner, and errors were automatically categorized with a Czech-specific version of ERRANT released at https://github.com/ufal/errant_czech
The dataset was introduced in the paper Czech Grammar Error Correction with a Large and Diverse Corpus that was accepted to TACL. Until published in TACL, see the arXiv version: https://arxiv.org/pdf/2201.05590.pdf
Grammar Error Correction Corpus for Czech (GECCC) consists of 83 058 sentences and covers four diverse domains, including essays written by native students, informal website texts, essays written by Romani ethnic minority children and teenagers and essays written by nonnative speakers. All domains are professionally annotated for GEC errors in a unified manner, and errors were automatically categorized with a Czech-specific version of ERRANT released at https://github.com/ufal/errant_czech
The dataset was introduced in the paper Czech Grammar Error Correction with a Large and Diverse Corpus that was accepted to TACL. Until published in TACL, see the arXiv version: https://arxiv.org/pdf/2201.05590.pdf
This version fixes double annotation errors in train and dev M2 files, and also contains more metadata information.
The GrandStaff-LMX dataset is based on the GrandStaff dataset described in the "End-to-end optical music recognition for pianoform sheet music" paper by Antonio Ríos-Vila et al., 2023, https://doi.org/10.1007/s10032-023-00432-z .
The GrandStaff-LMX dataset contains MusicXML and Linearized MusicXML encodings of all systems from the original datase, suitable for evaluation with the TEDn metric. It also contains the GrandStaff official train/dev/split.
The LatinISE corpus is a text corpus collected from the LacusCurtius, Intratext and Musisque Deoque websites. Corpus texts have rich metadata containing information as genre, title, century or specific date.
This Latin corpus was built by Barbara McGillivray.
The file represents a text corpus in the context of Arabic spell checking, where a group of persons edited different files, and all of the committed spelling errors by these persons have been recorded. A comprehensive representation these persons’ profile has been considered: male, female, old-aged, middle-aged, young-aged, high and low computer usage users, etc. Through this work, we aim to help researchers and those interested in Arabic NLP by providing them with an Arabic spell check corpus ready and open to exploitation and interpretation. This study also enabled the inventory of most spelling mistakes made by editors of Arabic texts. This file contains the following sections (tags): people – documents they printed – types of possible errors – errors they made. Each section (tag) contains some data that explains its details and its content, which helps researchers extracting research-oriented results. The people section contains basic information about each person and its relationship of using the computer, while the documents section clarifies all sentences in each document with the numbering of each sentence to be used in the errors section that was committed. We are also adding the “type of errors” section in which we list all the possible errors with their description in the Arabic language and give an illustrative example.
OLiMPiC: OpenScore Lieder Linearized MusicXML Piano Corpus is a dataset containing synthetic and scanned images of pianoform music scores. The scores and the scanned images originate from the OpenScore Lieder Corpus https://github.com/OpenScore/Lieder .
OLiMPiC contains the scores in MusicXML and Linearized MusicXML encoding, suitable for evaluation with the TEDn metric. The official train/dev/test split is also provided.