This corpus was originally created for performance testing (server infrastructure CorpusExplorer - see: diskurslinguistik.net / diskursmonitor.de). It includes the filtered database (German texts only) of CommonCrawl (as of March 2018). First, the URLs were filtered according to their top-level domain (de, at, ch). Then the texts were classified using NTextCat and only uniquely German texts were included in the corpus. The texts were then annotated using TreeTagger (token, lemma, part-of-speech). 2.58 million documents - 232.87 million sentences - 3.021 billion tokens. You can use CorpusExplorer (http://hdl.handle.net/11234/1-2634) to convert this data into various other corpus formats (XML, JSON, Weblicht, TXM and many more).
CsEnVi Pairwise Parallel Corpora consist of Vietnamese-Czech parallel corpus and Vietnamese-English parallel corpus. The corpora were assembled from the following sources:
- OPUS, the open parallel corpus is a growing multilingual corpus of translated open source documents.
The majority of Vi-En and Vi-Cs bitexts are subtitles from movies and television series.
The nature of the bitexts are paraphrasing of each other's meaning, rather than translations.
- TED talks, a collection of short talks on various topics, given primarily in English, transcribed and with transcripts translated to other languages. In our corpus, we use 1198 talks which had English and Vietnamese transcripts available and 784 talks which had Czech and Vietnamese transcripts available in January 2015.
The size of the original corpora collected from OPUS and TED talks is as follows:
CS/VI EN/VI
Sentence 1337199/1337199 2035624/2035624
Word 9128897/12073975 16638364/17565580
Unique word 224416/68237 91905/78333
We improve the quality of the corpora in two steps: normalizing and filtering.
In the normalizing step, the corpora are cleaned based on the general format of subtitles and transcripts. For instance, sequences of dots indicate explicit continuation of subtitles across multiple time frames. The sequences of dots are distributed differently in the source and the target side. Removing the sequence of dots, along with a number of other normalization rules, improves the quality of the alignment significantly.
In the filtering step, we adapt the CzEng filtering tool [1] to filter out bad sentence pairs.
The size of cleaned corpora as published is as follows:
CS/VI EN/VI
Sentence 1091058/1091058 1113177/1091058
Word 6718184/7646701 8518711/8140876
Unique word 195446/59737 69513/58286
The corpora are used as training data in [2].
References:
[1] Ondřej Bojar, Zdeněk Žabokrtský, et al. 2012. The Joy of Parallelism with CzEng 1.0. Proceedings of LREC2012. ELRA. Istanbul, Turkey.
[2] Duc Tam Hoang and Ondřej Bojar, The Prague Bulletin of Mathematical Linguistics. Volume 104, Issue 1, Pages 75–86, ISSN 1804-0462. 9/2015
We present the Czech Court Decisions Dataset (CCDD) -- a dataset of 300 manually annotated court decisions published by The Supreme Court of the Czech Republic and the Constitutional Court of the Czech Republic.
The Czech Legal Text Treebank (CLTT) is a collection of 1133 manually annotated dependency trees. CLTT consists of two legal documents: The Accounting Act (563/1991 Coll., as amended) and Decree on Double-entry Accounting for undertakers (500/2002 Coll., as amended).
BASIC INFORMATION
--------------------
Czech Text Document Corpus v 2.0 is a collection of text documents for automatic document classification in Czech language. It is composed of the text documents provided by the Czech News Agency and is freely available for research purposes. This corpus was created in order to facilitate a straightforward comparison of the document classification approaches on Czech data. It is particularly dedicated to evaluation of multi-label document classification approaches, because one document is usually labelled with more than one label. Besides the information about the document classes, the corpus is also annotated at the morphological layer.
The main part (for training and testing) is composed of 11,955 real newspaper articles. We provide also a development set which is intended to be used for tuning of the hyper-parameters of the created models. This set contains 2735 additional articles.
The total category number is 60 out of which 37 most frequent ones are used for classification. The reason of this reduction is to keep only the classes with the sufficient number of occurrences to train the models.
Technical Details
------------------------
Text documents are stored in the individual text files using UTF-8 encoding. Each filename is composed of the serial number and the list of the categories abbreviations separated by the underscore symbol and the .txt suffix. Serial numbers are composed of five digits and the numerical series starts from the value one.
For instance the file 00046_kul_nab_mag.txt represents the document file number 46 annotated by the categories kul (culture), nab (religion) and mag (magazine selection). The content of the document, i.e. the word tokens, is stored in one line. The tokens are separated by the space symbols.
Every text document was further automatically mophologically analyzed. This analysis includes lemmatization, POS tagging and syntactic parsing. The fully annotated files are stored in .conll files. We also provide the lemmatized form, file with suffix .lemma, and appropriate POS-tags, see .pos files. The tokenized version of the documents is also available in .tok files.
This corpus is available only for research purposes for free. Commercial use in any form is strictly excluded.
Diachronic corpus of Czech sized 3.45 million words (i.e. 4.1 million tokens). It contains 116 texts from the 14th-20th century period. The texts are transcribed, not transliterated. Diakorp v6 is provided in a CoNLL-U-like vertical format used as an input to the Manatee query engine. The data thus correspond to the corpus available via the KonText query interface to the registered users of CNC at http://www.korpus.cz
EngVallex 2.0 as a slightly updated version of EngVallex. It is the English counterpart of the PDT-Vallex valency lexicon, using the same view of valency, valency frames and the description of a surface form of verbal arguments. EngVallex contains links also to PropBank (English predicate-argument lexicon). The EngVallex lexicon is fully linked to the English side of the PCEDT parallel treebank(s), which is in fact the PTB re-annotated using the Prague Dependency Treebank style of annotation. The EngVallex is available in an XML format in our repository, and also in a searchable form with examples from the PCEDT. EngVallex 2.0 is the same dataset as the EngVallex lexicon packaged with the PCEDT 3.0 corpus, but published separately under a more permissive licence, avoiding the need for LDC licence which is tied to PCEDT 3.0 as a whole.
The representative full-text digitalized HetWiK corpus is composed of 140 manually annotated texts of the German Resistance between 1933 and 1945. This includes both well-known and relatively unknown documents, public writings, like pamphlets or memoranda, as well as private texts, e.g. letters, journal or prison entries and biographies. Thus the corpus represents the diverse groups as well as the heterogeneity of verbal resistance and allows the study of resistance in relation to the language usage.
The HetWiK corpus can be used free of charge. A detailed register of the individual texts and further information about the tagset can be found on the project-homepage (german). In addition to the CATMA5 XML-format we provide a standoff-JSON format and CEC6-Files (CorpusExplorer) - so you can export the HetWiK corpus in different formats.
Data
----
Hindi Visual Genome 1.0, a multimodal dataset consisting of text and images suitable for English-to-Hindi multimodal machine translation task and multimodal research. We have selected short English segments (captions) from Visual Genome along with associated images and automatically translated them to Hindi with manual post-editing, taking the associated images into account. The training set contains 29K segments. Further 1K and 1.6K segments are provided in a development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome.
Additionally, a challenge test set of 1400 segments will be released for the WAT2019 multi-modal task. This challenge test set was created by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity.
Dataset Formats
--------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Hindi Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
----------------
The statistics of the current release is given below.
Parallel Corpus Statistics
---------------------------
Dataset Segments English Words Hindi Words
------- --------- ---------------- -------------
Train 28932 143178 136722
Dev 998 4922 4695
Test 1595 7852 7535
Challenge Test 1400 8185 8665 (Released separately)
------- --------- ---------------- -------------
Total 32925 164137 157617
The word counts are approximate, prior to tokenization.
Citation
--------
If you use this corpus, please cite the following paper:
@article{hindi-visual-genome:2019,
title={{Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation}},
author={Parida, Shantipriya and Bojar, Ond{\v{r}}ej and Dash, Satya Ranjan},
journal={Computaci{\'o}n y Sistemas},
note={In print. Presented at CICLing 2019, La Rochelle, France},
year={2019},
}
This corpus consists of full transcriptions of both Democratic and Republican 2016 presidential candidate debates, with a special focus on the idiolects of Hillary Clinton and Donald Trump against the background of the speeches of other candidates for the post of president of the United States.
The transcriptions are sourced from the American Presidency Project at the University of California, Santa Barbara. Any use of the material requires a prior and explicit written permission by the project administrator (contact policy@ucsb.edu). This corpus material is now being shared with their kindly permission.