Abstract Lexical semantic change (LSC) detection investigates changes in word meaning over time, focusing on language use at the lexical-semantic level from a diachronic perspective. The field has made significant progress over the past two decades, driven by the increased availability of multilingual benchmarks, notable performance improvements, and growing interdisciplinary applications. In this paper, we review the evolution of LSC models and benchmark constructions within the context of popular shared tasks. By categorizing LSC models into generations defined by key components, our investigation suggests that performance breakthroughs have been largely driven by advances in semantic representations , transitioning from count-based models to recent transformer-based approaches. Notably, transformer models have established themselves as state-of-the-art by integrating Word-in-Context tasks, which emphasize semantic proximity in context . Furthermore, we review substantial studies that primarily leverage diachronic word embeddings to explore political, social, and cultural contexts beyond the linguistic domain. Our work provides valuable insights for future model development and encourages further interdisciplinary exploration within digital humanities and social sciences.
Abstract Natural language processing (NLP) technologies increasingly shape public life, yet their deployment for social good remains unevenly distributed across domains, languages, and geographies. This piece inaugurates the NLP for Social Good column in this journal. In this piece, I map the current state of NLP for Social Good (NLP4SG) across nine application domains. The picture that emerges is one of striking imbalance: AI harms, inclusion, and digital violence attract the bulk of research attention, while poverty, peacebuilding, and environmental protection remain critically underexplored. I argue that the field must address three structural gaps, domain coverage, linguistic diversity, and evaluation methodology, if NLP is to fulfil its potential as a force for equitable social progress. The piece concludes with five directions that I believe will define the next chapter of NLP4SG research.
Abstract Living languages continuously evolve to reflect the cultural changes of human societies. This evolution manifests through neologisms (new words) or the semantic change of existing words (new meanings for existing words). Understanding the meaning of words is vital for interpreting texts from different cultures (regionalisms or slang), domains (e.g., technical terms), or time periods. In computer science, this phenomenon is relevant to computational linguistics tasks such as machine translation, information retrieval, and question answering. Semantic change can impact the performance of these applications, making it important to understand and characterize these changes formally. This problem has recently attracted significant attention from the computational linguistics community. Several approaches can detect semantic changes with good precision, but more effort is needed to characterize how word meanings change and to determine how to mitigate the impact of this phenomenon. This survey provides a comprehensive overview of existing approaches to the characterization of semantic change . We also formally define three classes of characterization: change in dimension (whether a word’s meaning becomes broader or narrower), change in orientation (whether a word acquires a more pejorative or ameliorative sense), and change in relation (whether a word is used in a new figurative context, such as a metaphor or metonymy). We demonstrate the applicability of this formalism on existing corpora, summarize the key aspects of selected publications, and discuss current needs and trends in research on semantic change characterization.
Abstract Natural language processing (NLP) has moved from a specialized research field into the everyday infrastructure of writing, search, translation, education, journalism, public administration, and scientific work. This transition changes what counts as progress. Accuracy, fluency, and benchmark performance remain important, but they are no longer sufficient when language technologies shape knowledge, decisions, identities, and public trust. This column introduces Responsible NLP as a research orientation that integrates fairness, transparency, privacy, safety, cultural diversity, environmental awareness, and human agency across the full life cycle of language technologies. It argues that responsibility is not an external constraint on innovation, but a condition for meaningful and trustworthy innovation. Future research must therefore ask not only whether an NLP system works but also for whom it works, under which assumptions, with what risks, and with what forms of accountability.
