arXiv:2404.00176v3 Announce Type: replace Abstract: Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-level tasks: First, Word-in-Context (WiC) labels are derived for pairs of usages. Then, these labels are represented in a graph on which Word Sense Induction (WSI) is applied to derive sense clusters. Finally, LSCD labels are derived by comparing sense clusters over time. This modularity is reflected in most LSCD datasets and models. It also leads to a large heterogeneity in modeling options and tas
The paper introduces a new benchmark for Lexical Semantic Change Detection (LSCD), reflecting ongoing academic efforts to refine methods for tracking how word meanings evolve over time. This development aligns with the increasing sophistication of NLP models and the need for robust evaluation benchmarks.
A strategic reader should care because improved LSCD capabilities can enhance the understanding and tracking of conceptual shifts in language, which is critical for analyzing long-term trends in various domains, from cultural evolution to policy discourse, and for advancing AI's linguistic comprehension.
The introduction of the LSCD Benchmark offers a standardized and modular testbed, which can accelerate research and development in understanding diachronic word meaning tasks, potentially leading to more accurate and widely applicable models for semantic change detection.
- · AI researchers and developers
- · NLP community
- · Historians and social scientists using computational methods
- · Prior, less systematic LSCD evaluation methods
The benchmark provides a clearer path for comparing and improving Lexical Semantic Change Detection models.
Better LSCD models could lead to more nuanced AI analyses of historical texts, social media trends, and scientific literature for evolving concepts.
These advanced analyses might expose subtle, long-term shifts in public discourse or scientific paradigms, influencing policy and research funding.
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