Automatic semantic change detection aims to identify how word meanings shift over time, offering insights into both linguistic and societal change. Despite recent progress in computational lexical semantic change (LSC), existing benchmarks and methods struggle to capture bi-directional semantic change, particularly cases where words simultaneously gain and lose senses. This problem is especially challenging for words that have both slang and standard meanings. To address these gaps, we introduce two complementary benchmark datasets. The Bi-Directional Lexical Semantic Change (BD-LSC) dataset captures sense gain, sense loss, and stability across three time periods, enabling the study of complex semantic trajectories. The SlangTrack Word Sense Disambiguation (ST-WSD) dataset provides fine-grained, instance-level sense annotations for words combining slang and standard usages, supporting systematic benchmarking of WSD and semantic change detection models. Using these benchmarks, we systematically evaluate models across different methodological families: unsupervised clustering using contextualised embeddings, supervised machine learning, transformer-based models, and state-of-the-art large language models. Among the evaluated systems, the few-shot GPT-4o model achieved the strongest aggregate performance on Exact Sense Match (ESM) and multi-label accuracy; however, Macro-F1 scores near 0.5 across all systems show that rare slang senses remain difficult, which we identify as the central open challenge.

翻译：自动语义变化检测旨在识别词义随时间演变的过程，为语言及社会变迁提供洞见。尽管计算词汇语义变化（LSC）领域近期取得进展，现有基准测试与方法仍难以捕捉双向语义变化，尤其是词语同时获得与丧失义项的情况。这一问题对同时具有俚语和标准含义的词汇尤为棘手。为填补这些空白，我们提出两个互补性基准数据集。双向词汇语义变化（BD-LSC）数据集捕捉了三个时间段的义项获得、义项丧失及稳定性，支持复杂语义轨迹研究；SlangTrack 词义消歧（ST-WSD）数据集则提供融合俚语与标准用法的词汇细粒度实例级语义标注，支撑词义消歧（WSD）及语义变化检测模型的系统性基准测试。基于上述基准，我们系统评估了不同方法论家族的模型：基于上下文嵌入的无监督聚类、监督机器学习、基于Transformer的模型以及前沿大语言模型。在所有评估系统中，少样本GPT-4o模型在精准义项匹配（ESM）与多标签准确率上取得最优聚合性能；然而，各系统的宏F1分数均接近0.5，表明罕见俚语义项仍难以建模——我们将其定位为核心开放性挑战。