HSE at LSCDiscovery in Spanish: Clustering and Profiling for Lexical Semantic Change Discovery

Kashleva, Kseniia; Шеин, Александр Николаевич; Tukhtina, Elizaveta; Vydrina, Svetlana

doi:10.18653/v1/2022.lchange-1.21

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…HSE (Kashleva et al, 2022) This team participated with two different methods. The first consisted of fine-tuning BERT (Devlin et al, 2019) on the lemmatized versions of the corpora in order to extract embeddings of the target words separately for each period, which are then clustered using K-means.…”

Section: Participating Systemsmentioning

confidence: 99%

LSCDiscovery: A shared task on semantic change discovery and detection in Spanish

Zamora-Reina¹,

Bravo-Márquez²,

Schlechtweg³

2022

Preprint

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We present the first shared task on semantic change discovery and detection in Spanish and create the first dataset of Spanish words manually annotated for semantic change using the DURel framework (Schlechtweg et al., 2018). The task is divided in two phases: 1) Graded Change Discovery, and 2) Binary Change Detection. In addition to introducing a new language the main novelty with respect to the previous tasks consists in predicting and evaluating changes for all vocabulary words in the corpus. Six teams participated in phase 1 and seven teams in phase 2 of the shared task, and the best system obtained a Spearman rank correlation of 0.735 for phase 1 and an F1 score of 0.716 for phase 2. We describe the systems developed by the competing teams, highlighting the techniques that were particularly useful and discuss the limits of these approaches.

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Section: Participating Systemsmentioning

confidence: 99%

LSCDiscovery: A shared task on semantic change discovery and detection in Spanish

Zamora-Reina¹,

Bravo-Márquez²,

Schlechtweg³

2022

Preprint

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Studying word meaning evolution through incremental semantic shift detection

Periti,

Picascia,

Montanelli

et al. 2024

Lang Resources & Evaluation

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The study ofsemantic shift, that is, of how words change meaning as a consequence of social practices, events and political circumstances, is relevant in Natural Language Processing, Linguistics, and Social Sciences. The increasing availability of large diachronic corpora and advance in computational semantics have accelerated the development of computational approaches to detecting such shift. In this paper, we introduce a novel approach to tracing the evolution of word meaning over time. Our analysis focuses on gradual changes in word semantics and relies on an incremental approach to semantic shift detection (SSD) calledWhat is Done is Done(WiDiD). WiDiD leverages scalable and evolutionary clustering of contextualised word embeddings to detect semantic shift and capture temporaltransactionsin word meanings. Existing approaches to SSD: (a) significantly simplify the semantic shift problem to cover change between two (or a few) time points, and (b) consider the existing corpora as static. We instead treat SSD as an organic process in which word meanings evolve across tens or even hundreds of time periods as the corpus is progressively made available. This results in an extremely demanding task that entails a multitude of intricate decisions. We demonstrate the applicability of this incremental approach on a diachronic corpus of Italian parliamentary speeches spanning eighteen distinct time periods. We also evaluate its performance on seven popular labelled benchmarks for SSD across multiple languages. Empirical results show that our results are comparable to state-of-the-art approaches, while outperforming the state-of-the-art for certain languages.

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Lexical Semantic Change through Large Language Models: a Survey

Periti,

Montanelli

2024

ACM Comput. Surv.

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Lexical Semantic Change (LSC) is the task of identifying, interpreting, and assessing the possible change over time in the meanings of a target word. Traditionally, LSC has been addressed by linguists and social scientists through manual and time-consuming analyses, which have thus been limited in terms of the volume, genres, and time-frame that can be considered. In recent years, computational approaches based on Natural Language Processing have gained increasing attention to automate LSC as much as possible. Significant advancements have been made by relying on Large Language Models (LLMs), which can handle the multiple usages of the words and better capture the related semantic change. In this article, we survey the approaches based on LLMs for LSC and we propose a classification framework characterized by three dimensions: meaning representation , time-awareness , and learning modality . The framework is exploited to i) review the measures for change assessment, ii) compare the approaches on performance, and iii) discuss the current issues in terms of scalability, interpretability, and robustness. Open challenges and future research directions about the use of LLMs for LSC are finally outlined.

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HSE at LSCDiscovery in Spanish: Clustering and Profiling for Lexical Semantic Change Discovery

Cited by 3 publications

References 9 publications

LSCDiscovery: A shared task on semantic change discovery and detection in Spanish

LSCDiscovery: A shared task on semantic change discovery and detection in Spanish

Studying word meaning evolution through incremental semantic shift detection

Lexical Semantic Change through Large Language Models: a Survey

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