KAMOKO is a structured and commented french learner-corpus. It addresses the central structures of the French language from a linguistic perspective (18 different courses). The text examples in this corpus are annotated by native speakers. This makes this corpus a valuable resource for (1) advanced language practice/teaching and (2) linguistics research.
The KAMOKO corpus can be used free of charge. Information on the structure of the corpus and instructions on how to use it are presented in detail in the KAMOKO Handbook and a video-tutorial (both in german). In addition to the raw XML-data, we also offer various export formats (see ZIP files – supported file formats: CorpusExplorer, TXM, WebLicht, TreeTagger, CoNLL, SPEEDy, CorpusWorkbench and TXT).
KAMOKO is a structured and commented french learner-corpus. It addresses the central structures of the French language from a linguistic perspective (18 different courses). The text examples in this corpus are annotated by native speakers. This makes this corpus a valuable resource for (1) advanced language practice/teaching and (2) linguistics research.
The KAMOKO corpus can be used free of charge. Information on the structure of the corpus and instructions on how to use it are presented in detail in the KAMOKO Handbook and a video-tutorial (both in german). In addition to the raw XML-data, we also offer various export formats (see ZIP files – supported file formats: CorpusExplorer, TXM, WebLicht, TreeTagger, CoNLL, SPEEDy, CorpusWorkbench and TXT).
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.
Data
-----
We have collected English-Odia parallel data for the purposes of NLP
research of the Odia language.
The data for the parallel corpus was extracted from existing parallel
corpora such as OdiEnCorp 1.0 and PMIndia, and books which contain both
English and Odia text such as grammar and bilingual literature books. We
also included parallel text from multiple public websites such as Odia
Wikipedia, Odia digital library, and Odisha Government websites.
The parallel corpus covers many domains: the Bible, other literature,
Wiki data relating to many topics, Government policies, and general
conversation. We have processed the raw data collected from the books,
websites, performed sentence alignments (a mix of manual and automatic
alignments) and released the corpus in a form suitable for various NLP
tasks.
Corpus Format
-------------
OdiEnCorp 2.0 is stored in simple tab-delimited plain text files, each
with three tab-delimited columns:
- a coarse indication of the domain
- the English sentence
- the corresponding Odia sentence
The corpus is shuffled at the level of sentence pairs.
The coarse domains are:
books ... prose text
dict ... dictionaries and phrasebooks
govt ... partially formal text
odiencorp10 ... OdiEnCorp 1.0 (mix of domains)
pmindia ... PMIndia (the original corpus)
wikipedia ... sentences and phrases from Wikipedia
Data Statistics
---------------
The statistics of the current release are given below.
Note that the statistics differ from those reported in the paper due to
deduplication at the level of sentence pairs. The deduplication was
performed within each of the dev set, test set and training set and
taking the coarse domain indication into account. It is still possible
that the same sentence pair appears more than once within the same set
(dev/test/train) if it came from different domains, and it is also
possible that a sentence pair appears in several sets (dev/test/train).
Parallel Corpus Statistics
--------------------------
Dev Dev Dev Test Test Test Train Train Train
Sents # EN # OD Sents # EN # OD Sents # EN # OD
books 3523 42011 36723 3895 52808 45383 3129 40461 35300
dict 3342 14580 13838 3437 14807 14110 5900 21591 20246
govt - - - - - - 761 15227 13132
odiencorp10 947 21905 19509 1259 28473 24350 26963 704114 602005
pmindia 3836 70282 61099 3836 68695 59876 30687 551657 486636
wikipedia 1896 9388 9385 1917 21381 20951 1930 7087 7122
Total 13544 158166 140554 14344 186164 164670 69370 1340137 1164441
"Sents" are the counts of the sentence pairs in the given set (dev/test/train)
and domain (books/dict/...).
"# EN" and "# OD" are approximate counts of words (simply space-delimited,
without tokenization) in English and Odia
The total number of sentence pairs (lines) is 13544+14344+69370=97258. Ignoring
the set and domain and deduplicating again, this number drops to 94857.
Citation
--------
If you use this corpus, please cite the following paper:
@inproceedings{parida2020odiencorp,
title={OdiEnCorp 2.0: Odia-English Parallel Corpus for Machine Translation},
author={Parida, Shantipriya and Dash, Satya Ranjan and Bojar, Ond{\v{r}}ej and Motlicek, Petr and Pattnaik, Priyanka and Mallick, Debasish Kumar},
booktitle={Proceedings of the WILDRE5--5th Workshop on Indian Language Data: Resources and Evaluation},
pages={14--19},
year={2020}
}
OpenLegalData is a free and open platform that makes legal documents and information available to the public. The aim of this platform is to improve the transparency of jurisprudence with the help of open data and to help people without legal training to understand the justice system. The project is committed to the Open Data principles and the Free Access to Justice Movement.
OpenLegalData's DUMP as of 2022-10-18 was used to create this corpus. The data was cleaned, automatically annotated (TreeTagger: POS & Lemma) and grouped based on the metadata (jurisdiction - BundeslandID - sub-size if applicable - ex: Verwaltungsgerichtsbarkeit_11_05.cec6.gz - jurisdiction: administrative jurisdiction, BundeslandID = 11 - sub-corpus = 05). Sub-corpora are randomly split into 50 MB each.
Corpus data is available in CEC6 format. This can be converted into many different corpus formats - use the software www.CorpusExplorer.de if necessary.
Preamble 1.0 is a multilingual annotated corpus of the preamble of the EU REGULATION 2020/2092 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL. The corpus consists of four language versions of the preamble (Czech, English, French, Polish), each of them annotated with sentence subjects.
The data were annotated in the Brat tool (https://brat.nlplab.org/) and are distributed in the Brat native format, i.e. each annotated preamble is represented by the original plain text and a stand-off annotation file.
This is the first release of the UFAL Parallel Corpus of North Levantine, compiled by the Institute of Formal and Applied Linguistics (ÚFAL) at Charles University within the Welcome project (https://welcome-h2020.eu/). The corpus consists of 120,600 multiparallel sentences in English, French, German, Greek, Spanish, and Standard Arabic selected from the OpenSubtitles2018 corpus [1] and manually translated into the North Levantine Arabic language. The corpus was created for the purpose of training machine translation for North Levantine and the other languages.