Texts in 107 languages from the W2C corpus (http://hdl.handle.net/11858/00-097C-0000-0022-6133-9), first 1,000,000 tokens per language, tagged by the delexicalized tagger described in Yu et al. (2016, LREC, Portorož, Slovenia).
Texts in 107 languages from the W2C corpus (http://hdl.handle.net/11858/00-097C-0000-0022-6133-9), first 1,000,000 tokens per language, tagged by the delexicalized tagger described in Yu et al. (2016, LREC, Portorož, Slovenia).
Changes in version 1.1:
1. Universal Dependencies tagset instead of the older and smaller Google Universal POS tagset.
2. SVM classifier trained on Universal Dependencies 1.2 instead of HamleDT 2.0.
3. Balto-Slavic languages, Germanic languages and Romance languages were tagged by classifier trained only on the respective group of languages. Other languages were tagged by a classifier trained on all available languages. The "c7" combination from version 1.0 is no longer used.
The NottDeuYTSch corpus contains over 33 million words taken from approximately 3 million YouTube comments from videos published between 2008 to 2018 targeted at a young, German-speaking demographic and represents an authentic language snapshot of young German speakers. The corpus was proportionally sampled based on video category and year from a database of 112 popular German-speaking YouTube channels in the DACH region for optimal representativeness and balance and contains a considerable amount of associated metadata for each comment that enable further longitudinal cross-sectional analyses.
The NottDeuYTSch corpus contains over 33 million words taken from approximately 3 million YouTube comments from videos published between 2008 to 2018 targeted at a young, German-speaking demographic and represents an authentic language snapshot of young German speakers. The corpus was proportionally sampled based on video category and year from a database of 112 popular German-speaking YouTube channels in the DACH region for optimal representativeness and balance and contains a considerable amount of associated metadata for each comment that enable further longitudinal cross-sectional analyses.
Wikipedia plain text data obtained from Wikipedia dumps with WikiExtractor in February 2018.
The data come from all Wikipedias for which dumps could be downloaded at [https://dumps.wikimedia.org/]. This amounts to 297 Wikipedias, usually corresponding to individual languages and identified by their ISO codes. Several special Wikipedias are included, most notably "simple" (Simple English Wikipedia) and "incubator" (tiny hatching Wikipedias in various languages).
For a list of all the Wikipedias, see [https://meta.wikimedia.org/wiki/List_of_Wikipedias].
The script which can be used to get new version of the data is included, but note that Wikipedia limits the download speed for downloading a lot of the dumps, so it takes a few days to download all of them (but one or a few can be downloaded fast).
Also, the format of the dumps changes time to time, so the script will probably eventually stop working one day.
The WikiExtractor tool [http://medialab.di.unipi.it/wiki/Wikipedia_Extractor] used to extract text from the Wikipedia dumps is not mine, I only modified it slightly to produce plaintext outputs [https://github.com/ptakopysk/wikiextractor].
Universal Derivations (UDer) is a collection of harmonized lexical networks capturing word-formation, especially derivational relations, in a cross-linguistically consistent annotation scheme for many languages. The annotation scheme is based on a rooted tree data structure, in which nodes correspond to lexemes, while edges represent derivational relations or compounding. The current version of the UDer collection contains twenty-seven harmonized resources covering twenty different languages.
Universal Derivations (UDer) is a collection of harmonized lexical networks capturing word-formation, especially derivational relations, in a cross-linguistically consistent annotation scheme for many languages. The annotation scheme is based on a rooted tree data structure, in which nodes correspond to lexemes, while edges represent derivational relations or compounding. The current version of the UDer collection contains thirty-one harmonized resources covering twenty-one different languages.
Universal Segmentations (UniSegments) is a collection of lexical resources capturing morphological segmentations harmonised into a cross-linguistically consistent annotation scheme for many languages. The annotation scheme consists of simple tab-separated columns that stores a word and its morphological segmentations, including pieces of information about the word and the segmented units, e.g., part-of-speech categories, type of morphs/morphemes etc. The current public version of the collection contains 38 harmonised segmentation datasets covering 30 different languages.
A set of corpora for 120 languages automatically collected from wikipedia and the web.
Collected using the W2C toolset: http://hdl.handle.net/11858/00-097C-0000-0022-60D6-1