Research on the Mining and Visualization Analysis of Semantic Evolution Patterns of English Name Rotation Words Based on Corpus
TL;DR
This study constructs a corpus-based research framework that integrates corpus retrieval, semantic annotation, pattern mining, and interactive visualization for semantic evolution research and confirms the feasibility of combining corpus analysis with interactive visualization for semantic evolution research.
Abstract
Noun-to-verb conversion is a common form of English word-class conversion, and its semantic evolution reflects important mechanisms of lexical innovation. Existing studies often lack systematic mining of semantic evolution patterns and rely on static or single-dimensional visualization. This study constructs a corpus-based research framework that integrates corpus retrieval, semantic annotation, pattern mining, and interactive visualization. Typical noun-to-verb samples are selected from the British National Corpus, the Corpus of Contemporary American English, and the Oxford historical English corpus. Based on explicit semantic mapping rules, frame semantics, and dependency theory, the study identifies four core evolution patterns: metaphorical extension, metonymic mapping, semantic generalization, and semantic narrowing. Visualization using CiteSpace and ECharts presents pattern distribution, diachronic evolution trajectories, and semantic association networks. The results confirm the feasibility of combining corpus analysis with interactive visualization for semantic evolution research. The methodology can also support technical terminology tracking and semantic indexing in engineering corpora, including antenna systems, electromagnetic waves, and propagation-related texts.