# Metaphors' journeys across time and genre: tracking the evolution of literary metaphors with temporal embeddings

arXiv: https://arxiv.org/abs/2602.13701 · HTML: https://mlsanity.com/paper/2602.13701/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 74 references that are in the map.
- First posted on arXiv 14 Feb 2026.

## Builds on (most cited first, 7 of 7)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [Statistically Significant Detection of Linguistic Change](https://mlsanity.com/paper/1411.3315/) (2014)
- [Training Temporal Word Embeddings with a Compass](https://mlsanity.com/paper/1906.02376/) (2019)
- [Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods](https://mlsanity.com/paper/2001.07740/) (2020)
- [Frequency patterns of semantic change: Corpus-based evidence of a near-critical dynamics in language change](https://mlsanity.com/paper/1703.00203/) (2017)
- [Figurative Archive: an open dataset and web-based application for the study of metaphor](https://mlsanity.com/paper/2503.00444/) (2025)

## Built on by (most cited first, 1 of 1)

- [CAMMAR: Culture-Aware Matryoshka for Metaphorical Arabic Representations](https://mlsanity.com/paper/2607.15847/) (2026)

## Abstract (arXiv)

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.