In corpus linguistics, registers–language varieties suited to different contexts–have traditionally been defined by their situations of use, yet recent studies reveal significant situational variation within registers. Previous quantitative studies, however, have been limited to English, leaving this variation in other languages largely unexplored. To address this gap, we apply a quantitative situational analysis to a large multilingual web register corpus, using large language models (LLMs) to annotate texts in English, Finnish, French, Swedish, and Turkish for 23 situational parameters. Using clustering techniques, we identify six situational text types, such as “Advice”, “Opinion” and “Marketing”, each characterized by distinct situational features. We explore the relationship between these text types and traditional register categories, finding partial alignment, though no register maps perfectly onto a single cluster. These results support the quantitative approach to situational analysis and are consistent with earlier findings for English. Cross-linguistic comparisons show that language accounts for only a small part of situational variation within registers, suggesting registers are situationally similar across languages. This study demonstrates the utility of LLMs in multilingual register analysis and deepens our understanding of situational variation within registers.

We explore cross-lingual transfer of register classification for web documents. Registers, that is, text varieties such as blogs or news are one of the primary predictors of linguistic variation and thus affect the automatic processing of language. We introduce two new register-annotated corpora, FreCORE and SweCORE, for French and Swedish. We demonstrate that deep pre-trained language models perform strongly in these languages and outperform previous state-of-the-art in English and Finnish. Specifically, we show 1) that zero-shot cross-lingual transfer from the large English CORE corpus can match or surpass previously published monolingual models, and 2) that lightweight monolingual classification requiring very little training data can reach or surpass our zero-shot performance. We further analyse classification results finding that certain registers continue to pose challenges in particular for cross-lingual transfer.

The web presents unprecedented opportunities for large-scale collection of text in many languages. However, two critical steps in the development of web corpora remain challenging: the identification of clean text from source HTML and the assignment of genre or register information to the documents. In this paper, we evaluate a multilingual approach to this end. Our starting points are the Swedish and French Common Crawl datasets gathered for the 2017 CoNLL shared task, particularly the URLs. We 1) fetch HTML pages based on the URLs and run boilerplate removal, 2) train a classifier to further clean out undesired text fragments, and 3) annotate text registers. We compare boilerplate removal against the CoNLL texts, and find an improvement. For the further cleaning of undesired material, the best results are achieved using Multilingual BERT with monolingual fine-tuning. However, our results are promising also in a cross-lingual setting, without fine-tuning on the target language. Finally, the register annotations show that most of the documents belong to a relatively small set of registers, which are relatively similar in the two languages. A number of additional flags in the annotation are, however, necessary to reflect the wide range of linguistic variation associated with the documents.