We introduce UniversalCEFR, a large-scale multilingual multidimensional dataset of texts annotated according to the CEFR (Common European Framework of Reference) scale in 13 languages. To enable open research in both automated readability and language proficiency assessment, UniversalCEFR comprises 505,807 CEFR-labeled texts curated from educational and learner-oriented resources, standardized into a unified data format to support consistent processing, analysis, and modeling across tasks and languages. To demonstrate its utility, we conduct benchmark experiments using three modelling paradigms: a) linguistic feature-based classification, b) fine-tuning pre-trained LLMs, and c) descriptor-based prompting of instruction-tuned LLMs. Our results further support using linguistic features and fine-tuning pretrained models in multilingual CEFR level assessment. Overall, UniversalCEFR aims to establish best practices in data distribution in language proficiency research by standardising dataset formats and promoting their accessibility to the global research community.

We introduce FreeTxt, a free and open-source web-based tool designed to support the analysis and visualisation of multilingual qualitative survey data, with a focus on low-resource languages. Developed in collaboration with stakeholders, FreeTxt integrates established techniques from corpus linguistics with modern natural language processing methods in an intuitive interface accessible to non-specialists. The tool currently supports bilingual processing and visualisation of English and Welsh responses, with ongoing extensions to other languages such as Vietnamese. Key functionalities include semantic tagging via PyMUSAS, multilingual sentiment analysis, keyword and collocation visualisation, and extractive summarisation. User evaluations with cultural heritage institutions demonstrate the system’s utility and potential for broader impact.

Here we present SENTimental, a simple and fast web-based, mobile-friendly tool for capturing sentiment annotations from participants and citizen scientist volunteers to create training and testing data for low-resource languages. In contrast to existing tools, we focus on assigning broad values to segments of text over specific tags for tokens or spans to build datasets for training and testing LLMs. The SENTimental interface minimises barriers to entry with a goal of maximising the time a user spends in a flow state whereby they are able to quickly and accurately rate each text fragment without being distracted by the complexity of the interface. Designed from the outset to handle multilingual representations, SENTimental allows for parallel corpus data to be presented to the user and switched between instantly for immediate comparison. As such this allows for users in any loaded languages to contribute to the data gathered, building up comparable rankings in a simple structured dataset for later processing.

As part of the effort to increase the availability of Welsh digital technology, this paper introduces the first human vs metrics Welsh summarisation evaluation results and dataset, which we provide freely for research purposes to help advance the work on Welsh summarisation. The system summaries were created using an extractive graph-based Welsh summariser. The system summaries were evaluated by both human and a range of ROUGE metric variants (e.g. ROUGE 1, 2, L and SU4). The summaries and evaluation results will serve as benchmarks for the development of summarisers and evaluation metrics in other minority language contexts.

The days of large amorphous corpora collected with armies of Web crawlers and stored indefinitely are, or should be, coming to an end. There is a wealth of hidden linguistic information that is increasingly difficult to access, hidden in personal data that would be unethical and technically challenging to collect using traditional methods such as Web crawling and mass surveillance of online discussion spaces. Advances in privacy regulations such as GDPR and changes in the public perception of privacy bring into question the problematic ethical dimension of extracting information from unaware if not unwilling participants. Modern corpora need to adapt, be focused on testing specific hypotheses, and be respectful of the privacy of the people who generated its data. Our work focuses on using a distributed participatory approach and continuous informed consent to solve these issues, by allowing participants to voluntarily contribute their own censored personal data at a granular level. We evaluate our approach in a three-pronged manner, testing the accuracy of measurement of statistical measures of language with respect to standard corpus linguistics tools, evaluating the usability of our application with a participant involvement panel, and using the tool for a case study on health communication.

Welsh is an official language in Wales and is spoken by an estimated 884,300 people (29.2% of the population of Wales). Despite this status and estimated increase in speaker numbers since the last (2011) census, Welsh remains a minority language undergoing revitalisation and promotion by Welsh Government and relevant stakeholders. As part of the effort to increase the availability of Welsh digital technology, this paper introduces the first Welsh summarisation dataset, which we provide freely for research purposes to help advance the work on Welsh summarisation. The dataset was created by Welsh speakers through manually summarising Welsh Wikipedia articles. In addition, the paper discusses the implementation and evaluation of different summarisation systems for Welsh. The summarisation systems and results will serve as benchmarks for the development of summarisers in other minority language contexts.

While the application of word embedding models to downstream Natural Language Processing (NLP) tasks has been shown to be successful, the benefits for low-resource languages is somewhat limited due to lack of adequate data for training the models. However, NLP research efforts for low-resource languages have focused on constantly seeking ways to harness pre-trained models to improve the performance of NLP systems built to process these languages without the need to re-invent the wheel. One such language is Welsh and therefore, in this paper, we present the results of our experiments on learning a simple multi-task neural network model for part-of-speech and semantic tagging for Welsh using a pre-trained embedding model from FastText. Our model’s performance was compared with those of the existing rule-based stand-alone taggers for part-of-speech and semantic taggers. Despite its simplicity and capacity to perform both tasks simultaneously, our tagger compared very well with the existing taggers.

The last two decades have seen the development of various semantic lexical resources such as WordNet (Miller, 1995) and the USAS semantic lexicon (Rayson et al., 2004), which have played an important role in the areas of natural language processing and corpus-based studies. Recently, increasing efforts have been devoted to extending the semantic frameworks of existing lexical knowledge resources to cover more languages, such as EuroWordNet and Global WordNet. In this paper, we report on the construction of large-scale multilingual semantic lexicons for twelve languages, which employ the unified Lancaster semantic taxonomy and provide a multilingual lexical knowledge base for the automatic UCREL semantic annotation system (USAS). Our work contributes towards the goal of constructing larger-scale and higher-quality multilingual semantic lexical resources and developing corpus annotation tools based on them. Lexical coverage is an important factor concerning the quality of the lexicons and the performance of the corpus annotation tools, and in this experiment we focus on evaluating the lexical coverage achieved by the multilingual lexicons and semantic annotation tools based on them. Our evaluation shows that some semantic lexicons such as those for Finnish and Italian have achieved lexical coverage of over 90% while others need further expansion.

This paper outlines the new resource technologies, products and applications that have been constructed during the development of a multi-modal (MM hereafter) corpus tool on the DReSS project (Understanding New Forms of the Digital Record for e-Social Science), based at the University of Nottingham, England. The paper provides a brief outline of the DRS (Digital Replay System, the software tool at the heart of the corpus), highlighting its facility to display synchronised video, audio and textual data and, most relevantly, a concordance tool capable of interrogating data constructed from textual transcriptions anchored to video or audio, and from coded annotations of specific features of gesture-in-talk. This is complemented by a real-time demonstration of the DRS interface in-use as part of the LREC 2008 conference. This will serve to show the manner in which a system such as the DRS can be used to facilitate the assembly, storage and analysis of multi modal corpora, supporting both qualitative and quantitative approaches to the analysis of collected data.