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This paper proposes a novel framework for digital curation of Web corpora inorder to provide robust estimation of their parameters, such as theircomposition and the lexicon. In recent years language models pre-trained onlarge corpora emerged as clear winners in numerous NLP tasks, but no properanalysis of the corpora which led to their success has been conducted. Thepaper presents a procedure for robust frequency estimation, which helps inestablishing the core lexicon for a given corpus, as well as a procedure forestimating the corpus composition via unsupervised topic models and viasupervised genre classification of Web pages. The results of the digitalcuration study applied to several Web-derived corpora demonstrate theirconsiderable differences. First, this concerns different frequency bursts whichimpact the core lexicon obtained from each corpus. Second, this concerns thekinds of texts they contain. For example, OpenWebText contains considerablymore topical news and political argumentation in comparison to ukWac orWikipedia. The tools and the results of analysis have been released.

Know thy corpus! Robust methods for digital curation of Web corpora