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HindEnCorp 0.5

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Bojar, Ondřej; Diatka, Vojtěch; Straňák, Pavel; Tamchyna, Aleš and Zeman, Daniel, 2014, HindEnCorp 0.5, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), http://hdl.handle.net/11858/00-097C-0000-0023-625F-0.
Date issued
2014-03-21
Size
132300 sentences
Language(s)
Description
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.
Acknowledgement

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README.txt
Size
4.37 KB
Format
text/plain
Description
Text
MD5
0461668ddf034e11de3958528b64962f
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    HindEnCorp 0.5 and HindMonoCorp 0.5 File Formats
    ================================================
    
    This file describes the file formats of the Hindi-English and Hindi-only
    corpora released in 2014 under the names HindEnCorp 0.5 and HindMonoCorp 0.5.
    
    More details about the preparation of the corpora can be found in the paper:
    
      Ondřej Bojar, Vojtěch Diatka, Pavel Rychlý, Pavel Straňák, Aleš Tamchyna
      and Dan Zeman. HindEnCorp - Hindi-English and Hindi-only Corpus for
      Machine Translation. In Proc. of LREC 2014. Reykjavik, Iceland. ISBN
      978-2-9517408-8-4. ELRA. 2014.
    
    or on the corpora web page:
      http://ufal.mff.cuni.cz/hindencorp
    
    Please cite this paper if you make any use of the corpora. BibTeX citation
    format below.
    
    
    Common Properties
    -----------------
    
    All the files are plain text:
    
    - compressed with gzip
    - encoded in UTF-8
    - with unix line breaks (LF)
    - with tab-delimited columns
    
    The monolingual and parallel corpora have different columns.
    
    The actual corpus text is stored in one (monolingual corpus) or two (parallel
    corpus) of the columns.
    
    
    Plaintext vs. Export File Format
    --------------------------------
    
    Both the monolingual and the parallel corpus come in a simple plain text format
    and in a tokenized, tagged and lemmatized format.
    
    The plaintext format preserves the original tokenization (as much as possible
    given the diverse sources included in our corpus).
    
    The 'export' format is tokenized and represents each token as a '|'-delimited
    triple of: the word form, the lemma, and part-of-speech tag. If there was the
    character '|' (this character is also used instead of the proper Devanagari
    Danda in some sources), we escape it as '&pipe;'.
    
    There is exactly the same number of lines in the plaintext and export file
    formats.
    
    
    HindEnCorp Columns
    ------------------
    
    The files hindencorp05.plaintext.gz and hindencorp05.export.gz each contain the
    parallel corpus and differ only in the processing of the corpus texts. The
    files have these columns:
    
    - sou . . .
Name
hindencorp05.export.gz
Size
43.34 MB
Format
application/x-gzip
Description
gzip Archive
MD5
192ca33c840826a78832280839ba3628
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Name
hindencorp05.plaintext.gz
Size
22.79 MB
Format
application/x-gzip
Description
gzip Archive
MD5
512d754320c445bd9eb5c4912fee6844
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