Know thy corpus! Robust methods for digital curation of Web corpora
This addresses the problem of understanding corpus characteristics for NLP researchers and practitioners, but it is incremental as it builds on existing curation methods.
The paper tackles the lack of proper analysis of Web corpora used for training language models by proposing a framework for digital curation to robustly estimate parameters like composition and lexicon, demonstrating considerable differences in core lexicon and text types across corpora such as OpenWebText, ukWac, and Wikipedia.
This paper proposes a novel framework for digital curation of Web corpora in order to provide robust estimation of their parameters, such as their composition and the lexicon. In recent years language models pre-trained on large corpora emerged as clear winners in numerous NLP tasks, but no proper analysis of the corpora which led to their success has been conducted. The paper presents a procedure for robust frequency estimation, which helps in establishing the core lexicon for a given corpus, as well as a procedure for estimating the corpus composition via unsupervised topic models and via supervised genre classification of Web pages. The results of the digital curation study applied to several Web-derived corpora demonstrate their considerable differences. First, this concerns different frequency bursts which impact the core lexicon obtained from each corpus. Second, this concerns the kinds of texts they contain. For example, OpenWebText contains considerably more topical news and political argumentation in comparison to ukWac or Wikipedia. The tools and the results of analysis have been released.