Technology Category
Natural Language Processing: Lexical Semantics and MorphologyCognitive Modeling & Cognitive Systems: AnalogySearch and Optimization: Metareasoning and Metaheuristics
Metaphors'journeys across time and genre: tracking the evolution of literary metaphors with temporal embeddings
🤖 AI Summary
This study addresses the lack of empirical investigation into the diachronic evolution of literary metaphor processing difficulty. It pioneers the application of diachronic distributional semantic models to metaphor research, training word embeddings on a 124-million-token Italian corpus spanning the 19th to the 21st centuries. By quantifying changes in structural features—such as semantic similarity, vector consistency, and semantic neighborhood density—across 515 literary metaphors in both literary and non-literary texts, the analysis reveals that while overall metaphor processing difficulty remains stable over time, it significantly increases in modern literature yet becomes markedly more accessible in contemporary online language. These findings elucidate the interactive effects of genre and historical period on metaphor readability and corroborate the influence of stylistic simplification in modern literature and the high creativity of digital discourse on metaphor accessibility.
Technology Category
Application Category
📝 Abstract
Metaphors are a distinctive feature of literary language, yet they remain less studied experimentally than everyday metaphors. Moreover, previous psycholinguistic and computational approaches overlooked the temporal dimension, although many literary metaphors were coined centuries apart from contemporary readers. This study innovatively applies tools from diachronic distributional semantics to assess whether the processing costs of literary metaphors varied over time and genre. Specifically, we trained word embeddings on literary and nonliterary Italian corpora from the 19th and 21st centuries, for a total of 124 million tokens, and modeled changes in the semantic similarity between topics and vehicles of 515 19th-century literary metaphors, taking this measure as a proxy of metaphor processing demands. Overall, semantic similarity, and hence metaphor processing demands, remained stable over time. However, genre played a key role: metaphors appeared more difficult (i.e., lower topic-vehicle similarity) in modern literary contexts than in 19th-century literature, but easier (i.e., higher topic-vehicle similarity) in today's nonliterary language (e.g., the Web) than in 19th-century nonliterary texts. This pattern was further shaped by semantic features of metaphors'individual terms, such as vector coherence and semantic neighborhood density. Collectively, these findings align with broader linguistic changes in Italian, such as the stylistic simplification of modern literature, which may have increased metaphor processing demands, and the high creativity of the Web's language, which seems to render metaphor more accessible.
Problem
Research questions and friction points this paper is trying to address.
literary metaphors
temporal evolution
genre variation
processing difficulty
diachronic semantics
Innovation
Methods, ideas, or system contributions that make the work stand out.
diachronic word embeddings
literary metaphors
semantic similarity
metaphor processing
distributional semantics
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