The EBUContentGenre is a thesaurus containing the hierarchical description of various genres utilized in the TV broadcasting industry. This thesaurus is a part of a complex metadata specification called EBUCore intended for multifaceted description of audiovisual content. EBUCore (http://tech.ebu.ch/docs/tech/tech3293v1_3.pdf) is a set of descriptive and technical metadata based on the Dublin Core and adapted to media. EBUCore is the flagship metadata specification of European Broadcasting Union, the largest professional association of broadcasters around the world. It is developed and maintained by EBU's Technical Department (http://tech.ebu.ch). The translated thesaurus can be used for effective cataloguing of (mostly TV) audiovisual content and consequent development of systems for automatic cataloguing (topic/genre detection). and Technology Agency of the Czech Republic, project No. TA01011264
CzEng 1.0 is the fourth release of a sentence-parallel Czech-English corpus compiled at the Institute of Formal and Applied Linguistics (ÚFAL) freely available for non-commercial research purposes.
CzEng 1.0 contains 15 million parallel sentences (233 million English and 206 million Czech tokens) from seven different types of sources automatically annotated at surface and deep (a- and t-) layers of syntactic representation. and EuroMatrix Plus (FP7-ICT-2007-3-231720 of the EU and 7E09003+7E11051 of the Ministry of Education, Youth and Sports of the Czech Republic),
Faust (FP7-ICT-2009-4-247762 of the EU and 7E11041 of the Ministry of Education, Youth and Sports of the Czech Republic),
GAČR P406/10/P259,
GAUK 116310,
GAUK 4226/2011
CzEng 0.7 is a Czech-English parallel corpus compiled at the Institute of Formal and Applied Linguistics (ÚFAL), Charles University, Prague. The corpus contains no manual annotation. It is limited only to texts which have been already available in an electronic form and which are not protected by authors' rights in the Czech Republic. The main purpose of the corpus is to support Czech-English and English-Czech machine translation research with the necessary data. CzEng 0.7 consists of a large set of parallel textual documents mainly from the fields of European law, information technology, and fiction, all of them converted into a uniform XML-based file format and provided with automatic sentence alignment.
English models for MorphoDiTa, providing morphological analysis, morphological generation and part-of-speech tagging.
The morphological dictionary is created from Morphium and SCOWL (Spell Checker Oriented Word Lists), the PoS tagger is trained on WSJ (Wall Street Journal). and This work has been using language resources developed and/or stored and/or distributed by the LINDAT/CLARIN project of the Ministry of Education of the Czech Republic (project LM2010013).
The morphological POS analyzer development was supported by grant of the Ministry of Education, Youth and Sports of the Czech Republic No. LC536 "Center for Computational Linguistics". The morphological POS analyzer research was performed by Johanka Spoustová (Spoustová 2008; the Treex::Tool::EnglishMorpho::Analysis Perl module). The lemmatizer was implemented by Martin Popel (Popel 2009; the Treex::Tool::EnglishMorpho::Lemmatizer Perl module). The lemmatizer is based on morpha, which was released under LGPL licence as a part of RASP system (http://ilexir.co.uk/applications/rasp).
The tagger algorithm and feature set research was supported by the projects MSM0021620838 and LC536 of Ministry of Education, Youth and Sports of the Czech Republic, GA405/09/0278 of the Grant Agency of the Czech Republic and 1ET101120503 of Academy of Sciences of the Czech Republic. The research was performed by Drahomíra "johanka" Spoustová, Jan Hajič, Jan Raab and Miroslav Spousta.
English-Slovak parallel corpus consisting of several freely available corpora (Acquis [1], Europarl [2], Official Journal of the European Union [3] and part of OPUS corpus [4] – EMEA, EUConst, KDE4 and PHP) and downloaded website of European Commission [5]. Corpus is published in both in plaintext format and with an automatic morphological annotation.
References:
[1] http://langtech.jrc.it/JRC-Acquis.html/
[2] http://www.statmt.org/europarl/
[3] http://apertium.eu/data
[4] http://opus.lingfil.uu.se/
[5] http://ec.europa.eu/ and This work has been supported by the grant Euro-MatrixPlus (FP7-ICT-2007-3-231720 of the EU and 7E09003 of the Czech Republic)
EnTam is a sentence aligned English-Tamil bilingual corpus from some of the publicly available websites that we have collected for NLP research involving Tamil. The standard set of processing has been applied on the the raw web data before the data became available in sentence aligned English-Tamil parallel corpus suitable for various NLP tasks. The parallel corpus includes texts from bible, cinema and news domains.
HindEnCorp parallel texts (sentence-aligned) come from the following sources:
Tides, which contains 50K sentence pairs taken mainly from news articles. This dataset was originally col- lected for the DARPA-TIDES surprise-language con- test in 2002, later refined at IIIT Hyderabad and provided for the NLP Tools Contest at ICON 2008 (Venkatapathy, 2008).
Commentaries by Daniel Pipes contain 322 articles in English written by a journalist Daniel Pipes and translated into Hindi.
EMILLE. This corpus (Baker et al., 2002) consists of three components: monolingual, parallel and annotated corpora. There are fourteen monolingual sub- corpora, including both written and (for some lan- guages) spoken data for fourteen South Asian lan- guages. The EMILLE monolingual corpora contain in total 92,799,000 words (including 2,627,000 words of transcribed spoken data for Bengali, Gujarati, Hindi, Punjabi and Urdu). The parallel corpus consists of 200,000 words of text in English and its accompanying translations into Hindi and other languages.
Smaller datasets as collected by Bojar et al. (2010) include the corpus used at ACL 2005 (a subcorpus of EMILLE), a corpus of named entities from Wikipedia (crawled in 2009), and Agriculture domain parallel corpus.

For the current release, we are extending the parallel corpus using these sources:
Intercorp (Čermák and Rosen,2012) is a large multilingual parallel corpus of 32 languages including Hindi. The central language used for alignment is Czech. Intercorp’s core texts amount to 202 million words. These core texts are most suitable for us because their sentence alignment is manually checked and therefore very reliable. They cover predominately short sto- ries and novels. There are seven Hindi texts in Inter- corp. Unfortunately, only for three of them the English translation is available; the other four are aligned only with Czech texts. The Hindi subcorpus of Intercorp contains 118,000 words in Hindi.
TED talks 3 held in various languages, primarily English, are equipped with transcripts and these are translated into 102 languages. There are 179 talks for which Hindi translation is available.
The Indic multi-parallel corpus (Birch et al., 2011; Post et al., 2012) is a corpus of texts from Wikipedia translated from the respective Indian language into English by non-expert translators hired over Mechanical Turk. The quality is thus somewhat mixed in many respects starting from typesetting and punctuation over capi- talization, spelling, word choice to sentence structure. A little bit of control could be in principle obtained from the fact that every input sentence was translated 4 times. We used the 2012 release of the corpus.
Launchpad.net is a software collaboration platform that hosts many open-source projects and facilitates also collaborative localization of the tools. We downloaded all revisions of all the hosted projects and extracted the localization (.po) files.
Other smaller datasets. This time, we added Wikipedia entities as crawled in 2013 (including any morphological variants of the named entitity that appears on the Hindi variant of the Wikipedia page) and words, word examples and quotes from the Shabdkosh online dictionary. and LM2010013,
IDENTIC is an Indonesian-English parallel corpus for research purposes. The corpus is a bilingual corpus paired with English. The aim of this work is to build and provide researchers a proper Indonesian-English textual data set and also to promote research in this language pair. The corpus contains texts coming from different sources with different genres. and The research leading to these results has received funding from the European Commission’s 7th Framework Program under grant agreement no 238405 (CLARA) and by the grant LC536 Centrum Komputacni Lingvistiky of the Czech Ministry of Education.
Manual classification of errors of English-Slovak translation according to the classification introduced by Vilar et al. [1]. 50 sentences randomly selected from WMT 2011 test set [2] were translated by 3 MT systems described in [3] and MT errors were manually marked and classified. Reference translation is included.
References:
[1] David Vilar, Jia Xu, Luis Fernando D’Haro and Hermann Ney. Error Analysis of Machine Translation Output. In International Conference on Language Resources and Evaluation, pages 697-702. Genoa, Italy, May 2006.
[2] http://www.statmt.org/wmt11/evaluation-task.html
[3] Petra Galuščáková and Ondřej Bojar. Improving SMT by Using Parallel Data of a Closely Related Language. In Human Language Technologies - The Baltic Perspective - Proceedings of the Fifth International Conference Baltic HLT 2012, volume 247 of Frontiers in AI and Applications, pages 58-65, Amsterdam, Netherlands, October 2012. IOS Press. and This work has been supported by the grant Euro-MatrixPlus (FP7-ICT-2007-3-231720 of the EU and
7E09003 of the Czech Republic)
This corpora is part of Deliverable 5.5 of the European Commission project QTLeap FP7-ICT-2013.4.1-610516 (http://qtleap.eu).
The texts are Q&A interactions from the real-user scenario (batches 1 and 2). The interactions in this corpus are available in Basque, Bulgarian, Czech, English, Portuguese and Spanish.
The texts have been automatically annotated with NLP tools, including Word Sense Disambiguation, Named Entity Disambiguation and Coreference resolution. Please check deliverable D5.6 in http://qtleap.eu/deliverables for more information.