2023 EMNLP EMNLP 2023

From Diachronic to Contextual Lexical Semantic Change: Introducing Semantic Difference Keywords (SDKs) for Discourse Studies

Abstract

AbstractThis paper introduces the concept of Semantic Difference Keywords (SDKs). We define SDKs as keywords selected because of a comparatively high semantic difference between their use in two or more corpora. They are extracted by applying methods developed to identify diachronic Lexical Semantic Change. Like statistical keywords, most commonly used in quantitative discourse studies, SDKs capture the distinctiveness of a target corpus. However, they do not do so because they are used significantly more often or more consistently, but because they are used significantly differently. The case study presented in this paper shows that SDKs are successful in identifying concepts which are contested, i.e., sites of “semantic struggles” (CITATION). SDKs are therefore a useful contribution to (computational) discourse studies and text-based Digital Humanities more broadly.

🌉 Interdisciplinary Bridge — Interdisciplinary and Machine Learning
🧭 Keyword Pioneer — semantic difference keyword
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio