This paper introduces new models designed to improve the morpho-syntactic parsing of the five largest Latin treebanks in the Universal Dependencies (UD) framework. First, using two state-of-the-art parsers, Trankit and Stanza, along with our custom UD tagger, we train new models on the five treebanks both individually and by combining them into novel merged datasets. We also test the models on the CIRCSE test set. In an additional experiment, we evaluate whether this set can be accurately tagged using the novel LASLA corpus (https://github.com/CIRCSE/LASLA). Second, we aim to improve the results by combining the predictions of different models through an atomic morphological feature voting system. The results of our two main experiments demonstrate significant improvements, particularly for the smaller treebanks, with LAS scores increasing by 16.10 and 11.85%-points for UDante and Perseus, respectively (Gamba and Zeman, 2023a). Additionally, the voting system for morphological features (FEATS) brings improvements, especially for the smaller Latin treebanks: Perseus 3.15% and CIRCSE 2.47%-points. Tagging the CIRCSE set with our custom model using the LASLA model improves POS 6.71 and FEATS 11.04%-points respectively, compared to our best-performing UD PROIEL model. Our results show that larger datasets and ensemble predictions can significantly improve performance.

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.

Web-scale corpora present valuable research opportunities but often lack detailed metadata, making them challenging to use in linguistics and social sciences. This study tackles this problem by exploring automatic methods to classify web corpora into specific categories, focusing on text registers such as Interactive Discussion and literary genres such as Politics and Social Sciences. We train two machine learning models to classify documents from the large web-crawled OSCAR dataset: a register classifier using the multilingual, manually annotated CORE corpus, and a genre classifier using a dataset based on Kindle US&UK. Fine-tuned from XLM-R Large, the register and genre classifiers achieved F1-scores of 0.74 and 0.70, respectively. Our analysis includes evaluating the distribution of the predicted text classes and examining the intersection of genre-register pairs using topic modelling. The results show expected combinations between certain registers and genres, such as the Lyrical register often aligning with the Literature & Fiction genre. However, most registers, such as Interactive Discussion, are divided across multiple genres, like Engineering & Transportation and Politics & Social Sciences, depending on the discussion topic. This enriched metadata provides valuable insights and supports new ways of studying digital cultural heritage.

This article introduces a resource-efficient method for developing question-answer (QA) datasets by extracting QA pairs from web-scale data using machine learning (ML). Our method benefits from recent advances in web register (genre) identification and consists of two ML steps with an additional post-processing step. First, using XLM-R and the multilingual CORE web register corpus series with categories such as QA Forum, we train a multilingual classifier to retrieve documents that are likely to contain QA pairs from web-scale data. Second, we develop a NER-style token classifier to identify the QA text spans within these documents. To this end, we experiment with training on a semi-synthetic dataset built on top of the English LFQA, a small set of manually cleaned web QA pairs in English and Finnish, and a Finnish web QA pair dataset cleaned using ChatGPT. The evaluation of our pipeline demonstrates its capability to efficiently retrieve a substantial volume of QA pairs. While the approach is adaptable to any language given the availability of language models and extensive web data, we showcase its efficiency in English and Finnish, developing the first open, non-synthetic and non-machine translated QA dataset for Finnish – Turku WebQA – comprising over 200,000 QA pairs.