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.

Semantic textual similarity (STS) is to measure semantic equivalence between sentences; it plays an important role in natural language processing (NLP) tasks. The major core of STS is text representation. This paper studies how to obtain a sentence skeleton for text representation in STS. Unlike most existing syntax models, we propose a skeleton-based reinforcement learning method to identify the skeleton and construct sentence representations. Parallel networks are adopted to extract features of different dimensions in the sentence. In the framework of parallel networks, two sentence representation models are designed: context constrained LSTM (CC-LSTM) and Adorned skeleton LSTM (AS-LSTM). CC-LSTM builds the sentence representation by constraining the word context. AS-LSTM constructs the sentence representation through using the identified skeleton and its qualifiers. Our approach achieves good results without using external resources. Especially AS-LSTM, which outperforms the state-of-the-art without using external resources in the SICK dataset.

This study investigates the nuanced challenges of fine-grained word sense disambiguation (WSD) tasks with regular polysemy detection (RPD) of the named entity, focusing on evaluating the trade-offs between encoder and decoder-based model performance and computational efficiency. The datasets, including Chinese Wordnet 2.0 (CWN) as sense inventory, the Social Media Corpus (PTT) for user-generated content, and the Academia Sinica Balanced Corpus (ASBC) for formal linguistic data, were chosen to provide a diverse and representative framework for evaluating both common nouns and proper nouns with regular polysemy in Taiwan Mandarin. This analysis evaluated ten encoder- and decoder-based models, assessing their performance on two tasks. The encoder-based models demonstrate comparable accuracy to the decoder-based models on WSD tasks (77.5% vs. 78.5%), and similarly strong performance in RPD tasks (84.2% vs. 83.8%). On a large-scale all-words WSD task, the encoder model not only outperformed the decoder model but also generated substantially lower carbon emissions – an eight-fold reduction. These differences underscore the trade-offs between model architecture and task-specific performance, highlighting the necessity for balancing performance and energy efficiency in the design and application of language models, advocating for sustainable and eco-friendly practices in natural language processing development.

Twitter is one of the most widely used social networks globally but is also notorious for harbouring significant levels of hate speech. This paradox has garnered the attention of companies and governments concerned about how digital hate can fragment communities and incite real-world violence. While extensive research has focused on detecting hate speech, there is a lack of comprehensive analysis of the actors involved, their characteristics, and their interactions within the online environment. Common assumptions categorise users into merely two groups—haters and non-haters—overlooking the existence of other groups that may more accurately represent the dynamics of hate dissemination. Additionally, existing user classification models often rely on large volumes of tweet data, a limitation given the restricted access to the Twitter API. Social networks are also frequently visualised using graphs where edges represent only superficial relationships. This study addresses these gaps through five research questions. We propose formal user clustering methods and develop a classifier that uses exclusively profile attributes—information more readily obtainable from the network. We also introduce a more nuanced definition of interaction for graph edges, based on ideological support or opposition between users. To conduct our analysis, we have extracted two complementary datasets: (i) a keyword-based corpus of 3.3M Spanish tweets containing hate-associated terms, of which 1.6M unique tweets were retained after filtering and (ii) a user-based corpus comprising timelines of $\approx$3,000 users linked to hate speech, totalling over 3 M tweets. Our results reveal the existence of three primary user classes—haters, upstanders, and neutrals—in contrast to the conventional binary classification. We demonstrate that profile attributes are reliable indicators for automatically classifying users and find significant statistical differences between these classes. Finally, we develop a graph visualisation tool to assist authorities in analysing interactions among different user types, providing a useful exploratory tool to support the analysis of online hate and inform potential mitigation strategies.

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.