Ukrainian Trends: a daily-updated monitor corpus of news articles
The Ukrainian Trends corpus is a Ukrainian monitor corpus made up of news articles, Wikipedia and other sources that are regularly updated from their RSS feeds (newsfeeds). The Ukrainian trends corpus is updated daily with new texts and grows by about 1 million words each day. These regular updates enable you to use the diachronic analysis tool including the timeline show_chart function and study word usage changes.
Part-of-speech tagset
The Ukrainian Trends corpus is tagged by RFTagger using tagset.
How is the Ukrainian Trends corpus updated?
New texts are downloaded every four hours. Every Tuesday and Friday, all texts downloaded until the previous day are added to the corpus. Duplicate and near-duplicate texts within the same month are removed, but they are kept in different months, e.g. if the same text appears in June twice, only one instance is kept. If the same text appears once in June and once in July, both are kept.
Search the Ukrainian Trends corpus
Sketch Engine offers a range of tools to work with this Ukrainian Trends corpus of news articles.
Tools to work with the Ukrainian Trends corpus
A complete set of tools is available to work with this Ukrainian Trends corpus from news to generate:
- word sketch – Ukrainian 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 Ukrainian nouns, verbs, adjectives, etc. organized by frequency
- n-grams – frequency list of multi-word units
- concordance – examples in context
- trends – diachronic analysis automatically identifies neologisms and changes in use
- text type analysis – statistics of metadata in the corpus
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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