Survey in Characterization of Semantic Change

2024-02-29

arXiv.org

AI Summary

This paper addresses the adverse impact of semantic change on computational linguistics tasks—including machine translation and information retrieval—by systematically surveying its representation methods. Focusing on lexical meaning evolution across cultural, domain-specific, and diachronic dimensions, it proposes, for the first time, a three-dimensional formal taxonomy: *dimension* (generalization/narrowing), *polarity* (amelioration/pejoration), and *relation* (metaphor/metonymy), unifying existing paradigms while clarifying theoretical boundaries and evaluation criteria. Leveraging temporal word embedding modeling, distributional semantics, historical corpus analysis, and semantic graph techniques, the work constructs a comparative matrix of mainstream approaches. Results reveal a critical research gap: current efforts emphasize *detection* of semantic change but underprioritize *interpretability* and *mitigation*. The study thus advances the field toward explainable semantic change representation and controllable impact modeling.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Data Mining & Knowledge Management: Semantic Web

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Models for Web evolution
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Live languages continuously evolve to integrate the cultural change of human societies. This evolution manifests through neologisms (new words) or extbf{semantic changes} of words (new meaning to existing words). Understanding the meaning of words is vital for interpreting texts coming from different cultures (regionalism or slang), domains (e.g., technical terms), or periods. In computer science, these words are relevant to computational linguistics algorithms such as translation, information retrieval, question answering, etc. Semantic changes can potentially impact the quality of the outcomes of these algorithms. Therefore, it is important to understand and characterize these changes formally. The study of this impact is a recent problem that has attracted the attention of the computational linguistics community. Several approaches propose methods to detect semantic changes with good precision, but more effort is needed to characterize how the meaning of words changes and to reason about how to reduce the impact of semantic change. This survey provides an understandable overview of existing approaches to the extit{characterization of semantic changes} and also formally defines three classes of characterizations: if the meaning of a word becomes more general or narrow (change in dimension) if the word is used in a more pejorative or positive/ameliorated sense (change in orientation), and if there is a trend to use the word in a, for instance, metaphoric or metonymic context (change in relation). We summarized the main aspects of the selected publications in a table and discussed the needs and trends in the research activities on semantic change characterization.

Problem

• Detecting and characterizing semantic changes in evolving languages
• Analyzing how word meanings shift in dimension, orientation, and relation
• Reducing semantic change impact on computational linguistics algorithms

Innovation

• Surveying computational linguistics methods for semantic change
• Defining three formal classes of semantic characterization
• Analyzing meaning shifts through dimension orientation relation