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.
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.
This article deals with germanisms in Czech. Frequencies of 26 different new High German loanwords were analyzed in the Czech National Corpus. These borrowed words were standing in competition with their Czech synonyms. This comparison is used to study the question of whether germanisms or their equivalents in Czech are more used by native speakers. For this analysis new High German loanwords were deliberately selected in order to verify the actuality of the topic. But the major part of the study was examined in a diachronic period. This shows not only the current situation but in most cases the frequency of the selected loanwords throughout their existence. The calculations of the average frequency are made for each century (since 1650), and also in the recent modern period (from 1947 to 2008). and Článek se zabývá germanizmy v češtině. Prostřednictvím Českého národního korpusu byly zjišťovány různé frekvence 26 novohornoněmeckých výpůjček a jim konkurujících českých synonym. Článek se na základě frekvenčních srovnání snaží odpovědět na otázku, zda čeští rodilí mluvčí preferují germanizmy či dávají přednost jejich českým ekvivalentům. Článek analyzuje nejen aktuální situaci, ale ve většině případů ukazuje frekvenci vybraných germanizmů z diachronního hlediska, po celou dobu jejich existence. Byla vypočtena průměrná frekvence za každé století (od roku 1650), včetně posledního moderního období (od roku 1947 do roku 2008).
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}
}
onion (ONe Instance ONly) is a tool for removing duplicate parts from large collections of texts. The tool has been implemented in Python, licensed under New BSD License and made an open source software (available for download including the source code at http://code.google.com/p/onion/). It is being successfuly used for cleaning large textual corpora at Natural language processing centre at Faculty of informatics, Masaryk university Brno and it's industry partners. The research leading to this piece of software was published in author's Ph.D. thesis "Removing Boilerplate and Duplicate Content from Web Corpora". The deduplication algorithm is based on comparing n-grams of words of text. The author's algorithm has been shown to be more suitable for textual corpora deduplication than competing algorithms (Broder, Charikar): in addition to detection of identical or very similar (95 %) duplicates, it is able to detect even partially similar duplicates (50 %) still achieving great performace (further described in author's Ph.D. thesis). The unique deduplication capabilities and scalability of the algorithm were been demonstrated while building corpora of American Spanish, Arabic, Czech, French, Japanese, Russian, Tajik, and six Turkic languages consisting --- several TB of text documents were deduplicated resulting in corpora of 70 billions tokens altogether. and PRESEMT, Lexical Computing Ltd
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.
Corpus of informal spoken Czech sized 1 MW. It contains transcriptions of 221 recordings made in 2002–2006 in the whole of Bohemia. All the recordings were made in informal situations to ensure prototypically spontaneous spoken language. This means private environment, physical presence of speakers who know each other, unscripted speech and topic not given in advance. The total number of speakers is 754, the metadata include sociolinguistic information about them.
The corpus is provided in a (semi-XML) vertical format used as an input to the Manatee query engine. The data thus exactly correspond to the corpus available via query interface to registered users of the CNC. and Výzkumný záměr MSM0021620823 – Český národní korpus a korpusy dalších jazyků
The PADT project might be summarized as an open-ended activity of the Center for Computational Linguistics, the Institute of Formal and Applied Linguistics, and the Institute of Comparative Linguistics, Charles University in Prague, resting in multi-level annotation of Arabic language resources in the light of the theory of Functional Generative Description (Sgall et al., 1986; Hajičová and Sgall, 2003).
The Prague Dependency Treebank 2.0 (PDT 2.0) contains a large amount of Czech texts with complex and interlinked morphological (two million words), syntactic (1.5 MW) and complex semantic annotation (0.8 MW); in addition, certain properties of sentence information structure and coreference relations are annotated at the semantic level.
PDT 2.0 is based on the long-standing Praguian linguistic tradition, adapted for the current Computational Linguistics research needs. The corpus itself uses the latest annotation technology. Software tools for corpus search, annotation and language analysis are included. Extensive documentation (in English) is provided as well. and 1ET101120413 (Data a nástroje pro informační systémy) MSM 0021620838 (Moderní metody, struktury a systémy informatiky) 1ET101120503 (Integrace jazykových zdrojů za účelem extrakce informací z přirozených textů) 1P05ME752 (Vícejazyčný valenční a predikátový slovník přirozeného jazyka) LC536 (Centrum komputační lingvistiky)
The first edition of a speech corpus with a speech reconstruction layer (edited transcript).
The project of speech reconstruction of Czech and English has been started at UFAL together with the PIRE project in 2005, and has gradually grown from ideas to (first) annotation specification, annotation software and actual annotation. It is part of the Prague Dependency Treebank family of annotated corpus resources and tools, to which it adds the spoken language layer(s). and LC536; MSM0021620838; IST-034344; ME838