DWUGs-IT: Extending and Standardizing Lexical Semantic Change Detection for Italian
Authors: Pierluigi Cassotti, Pierpaolo Basile, Nina Tahmasebi (University of Gothenburg, Università di Bari Aldo Moro, 2024)
Topic: Latin Resources

Abstract
Lexical Semantic Change Detection (LSCD) is the task of determining whether a word has undergone a change in meaning over time. There has been a marked increase in interest in this task, accompanied by a corresponding growth in the scientific community involved in developing computational approaches to semantic change. In recent years, a number of resources have been made available for the evaluation of LSC models in a number of languages, including English, Swedish, German, Latin, Russian and Chinese. DIACR-ITA is the only existing resource for LSCD in Italian. However, DIACR-ITA has a different format from that used for other languages. In this paper, we present DWUGs-IT, which extends the DIACR-ITA dataset with additional target words and usage-sense pair annotations and adapts it to the DURel format, including the first implementation of a LSCD graded task for Italian.

Introduction
As is the case with both society and culture, language is subject to change over time. Two key factors cause such linguistic change. Firstly, there are purely evolutionary and linguistic considerations driven by the need for more efficient communication AUTHOR. One example of this is the use of abbreviations and acronyms, such as LOL (Laughing Out Loud), which have become commonplace on social media platforms. Secondly, changes in society and culture lead to changes in language. This can be seen, for example, in the adoption of a more inclusive language, as evidenced by grammatically gendered languages, including Italian and the introduction of {\schwa} to replace masculine and feminine endings AUTHOR. Language may undergo alteration at various levels, including morphological, syntactic, and semantic. Semantic change concerns the alteration of the meaning of words over time. The study of semantic change is a prominent area of research in Historical Linguistics, with the aim of investigating the linguistic mechanisms that characterize the change and the causes that trigger it. For instance, \citet{blank2012prinzipien} provides a broad study on the characterization of semantic change, identifying a number of different types of change, including metaphor, metonymy, generalization, specialization, co-hyponym transfer and auto-antonym. The English word bad, for example, has acquired an auto-antonym meaning, i.e. a meaning that is the opposite of its original meaning. In addition to its original connotation of poor quality or negative, it has also acquired the opposite connotation of good or cool. The term meat has undergone a process of specialization in its meaning, whereby it has shifted from referring to any kind of food in general to exclusively denoting the meat of animals consumed as food. While traditional linguistic methods are informative, they are often based on small, carefully curated samples. In contrast, linguistic analyses using computational models not only accelerate our understanding of language change but also provide broader and more detailed insights, thereby facilitating the study of vast corpora across a wider range of genres and time AUTHOR. From a computational perspective, two key challenges emerge in the study of semantic change: the modelling of word meanings over time and the detection of change AUTHOR. At the synchronic level, ignoring the temporal dimension with a focus on modern corpora, the Natural Language Processing community has made significant strides in modelling word meanings, with approaches such as Word Sense Disambiguation (WSD) AUTHOR playing a pivotal role. Computational modelling of semantic change introduces a significant level of complexity, as it necessitates the handling of meanings that are either extinct or novel in comparison to existing lexicographic resources, such as WordNet, as well as dynamically changing meaning representations. In recent years, great efforts have been made to advance the field of computational methods for Lexical Semantic Change Detection. With initiatives such as the Workshop on Computational Approaches to Historical Language Change AUTHOR promoting research in this field or shared tasks such as SemEval 2020 Task 1 AUTHOR, RuShiftEval AUTHOR, DIACR-ITA AUTHOR, or LSCD Discovery AUTHOR leading to the development of the first evaluation resources. DIACR-Ita, hosted in EVALITA 2020 AUTHOR, is the first shared task specifically created for the evaluation of models for Lexical Semantic Change in Italian. The majority of the evaluation resources follow a two-task approach: (1) a binary task, which requires the assignment of a word to either the changed or stable label, based on whether the word has undergone a change in meaning or not; and (2) a graded (ranking) task, which requires the sorting of words based on the extent of their change (over time). These labels are assigned on the basis of human-annotated data, typically in the form of a graded word-in-context task. %or something about how the "rest" of the data looks like because in the end of the next line you say that Diacr-ita does not release this data. Then it is good to know what it is and what it is good for. DIACR-Ita, however, diverges from the evaluation process employed in SemEval 2020 Task 1, RushiftEval and several other datasets that emerged subsequently. This results in a distinct configuration of the task and the released data. For example, DIACR-Ita only has a binary task but does not include a graded task. Moreover, only the target words with their gold truth labels were made available for the shared task, while the remaining data produced during the annotation process were not. In this paper, \item we release DWUGs-IT .}, a new dataset for Lexical Semantic Change Detection for Italian, which: \item extends the original DIACR-ITA %above you use DIACR-Ita not ITA with 12 new words; \item provides sense-annotated usages with the respective sense labels \item standardizes DIACR-ITA providing the data in the DURel format AUTHOR \item introduces the first LSC graded task for Italian \item we evaluate DWUGs-IT using XL-LEXEME AUTHOR, the state-of-the-art model for Lexical Semantic Change Detection AUTHOR

Conclusion
This paper presents DWUGs-IT, an extension and standardization of the Lexical Semantic Change Detection (LSCD) task for Italian, based on the existing DIACR-ITA dataset. The dataset is expanded with additional target words and its format is aligned with that of the resources used for other languages. This involves the introduction of the first graded task for Italian. The standardized dataset and the evaluation framework we provide can serve as a foundation for future research in LSCD for Italian. By aligning the Italian dataset with those of other languages, we facilitate cross-linguistic comparisons and contribute to the broader understanding of semantic change mechanisms. In addition, we provide a first evaluation of the state-of-the-art LSCD model, XL-LEXEME, for Italian and both show its effectiveness as well as set a baseline for future work. \noindent This work has in part been funded by the research program Change is Key! supported by Riksbankens Jubileumsfond (under reference number M21-0021). The computational resources were provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through grant agreement no. 2022-06725. \noindent We acknowledge the support of the PNRR project FAIR - Future AI Research (PE00000013), Spoke 6 - Symbiotic AI (CUP H97G22000210007) under the NRRP MUR program funded by the NextGenerationEU. \noindent We would also like to thank Tommaso Caselli, Annalina Caputo and Rossella Varvara, who contributed to the development of the DIACR-ITA resource, and Dominik Schlechtweg for valuable feedback on a preliminary draft of this work.