Our previous study found that generative LLMs can be successfully used to identify instances of schematic constructions (as defined in Construction Grammar) in unannotated L1 corpus data. This study tests the applicability of LLMs to also identify instances of constructions in unannotated L2 data. L2 learner corpora are notoriously difficult to annotate and query since they contain errors. Using LLMs can thus simplify the retrieval of construction data from L2 corpora. The identification of instances of constructions in L2 learner data has many possible uses in pedagogical applications of Construction Grammar and constructicography, like the identification of error-prone (properties of) constructions and the distribution of constructional instances across CEFR levels. Using the Estonian Nominal Quantifier Construction as the example construction and an Estonian CEFR-graded learner corpus as the source of L2 data, we tested several prompts and several models (OpenAI’s o3-mini, o3, gpt-5-mini and gpt-5, Google DeepMind’s Gemini Flash 2.5, Anthropic’s Claude Sonnet 4.5 and Opus 4.1). We found that the best model, gpt-5, achieved F1-scores from 0.90 to 0.96, depending on the level of detail of the prompt.

This article addresses methodological issues related to developing domain corpora and a terminological database from scratch. We present an ongoing project focused on creating an Estonian-English Remote Sensing Termbase. First, we describe the compilation process of the Estonian Remote Sensing Corpus 2022 , which served as the primary data source for the termbase. The corpus was compiled by crawling the web and adding files using the Corpus Query System Sketch Engine (Kilgarriff et al., 2004). In the next step, we employed the Term Extraction module (Kilgarriff et al., 2014; Fišer et al., 2016; Blahuš et al., 2023) to identify terms, which were subsequently registered in the Estonian Remote Sensing Termbase using the Dictionary Writing System Ekilex (Tavast et al., 2018). For each term, we provided definitions, variants, and usage contexts. In the final stage, remote sensing experts reviewed and edited the terms, their variants, and usage contexts. Finally, we provide insights and outline directions for future work in this area.

The paper presents the results of the project “Teacher’s Tools” (et Õpetaja tööriistad) published as a subpage of the new language portal Sõnaveeb developed by the Institute of the Estonian Language. The toolbox includes four modules: vocabulary, grammar, communicative language activities and text evaluation. The tools are aimed to help teachers and specialists of Estonian as a second language plan courses, create new educational materials, exercises and tests based on CEFR level descriptions.

Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.