skTenTen: Corpus of the Slovak Web
The Slovak Web Corpus (skTenTen) is a Slovak corpus made up of texts collected from the Internet. The corpus belongs to the TenTen corpus family which is a set of the web corpora built using the same method with a target size 10+ billion words. Sketch Engine currently provides access to TenTen corpora in more than 30 languages.
Data was crawled by the SpiderLing web spider in 2011 and comprised of almost 715 million words. Apart from the standard word, tag, lemma attributes, the corpus also contains an extra attribute called amblevel which is an integer number indicating the level of ambiguity of each word form. It is the number of possible POS-tags for given word form (from which the disambiguator selected one). Word sketches have been prepared by Vladimír Benko.
Detailed information about TenTen corpora is on the separate page Common TenTen corpora attributes.
Part-of-speech tagset
The skTenTen corpus was POS annotated by Ľ. Štúr Institute of Linguistics of Slovak Academy of Sciences with the same tagset from the National Slovak corpus.
Tools to work with the Slovak Web corpus
A complete set of Sketch Engine tools is available to work with this Slovak corpus to generate:
- word sketch – Slovak collocations categorized by grammatical relations
- thesaurus – synonyms and similar words for every word
- keywords – terminology extraction of one-word and multi-word units
- word lists – lists of Slovak nouns, verbs, adjectives etc. organized by frequency
- n-grams – frequency list of multi-word units
- concordance – examples in context
- text type analysis – statistics of metadata in the corpus
Changelog
version (13 September 2011)
- initial version – 876 million tokens
Bibliography
TenTen corpora
Jakubíček, M., Kilgarriff, A., Kovář, V., Rychlý, P., & Suchomel, V. (2013, July). The TenTen corpus family. In 7th International Corpus Linguistics Conference CL (pp. 125-127).
Suchomel, V., & Pomikálek, J. (2012). Efficient web crawling for large text corpora. In Proceedings of the seventh Web as Corpus Workshop (WAC7) (pp. 39-43).
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