Meanings are like Onions: a Layered Approach to Metaphor Processing

• URL: http://arxiv.org/abs/2507.10354v1
• Date: Mon, 14 Jul 2025 14:56:46 GMT
• Title: Meanings are like Onions: a Layered Approach to Metaphor Processing
• Authors: Silvia Cappa, Anna Sofia Lippolis, Stefano Zoia,
• Abstract summary: We propose a model of metaphor processing that treats meaning as an onion.<n>At the first level, metaphors are annotated through basic conceptual elements.<n>At the second level, we model conceptual combinations, linking components to emergent meanings.<n>At the third level, we introduce a pragmatic vocabulary to capture speaker intent, communicative function, and contextual effects.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Metaphorical meaning is not a flat mapping between concepts, but a complex cognitive phenomenon that integrates multiple levels of interpretation. In this paper, we propose a stratified model of metaphor processing that treats meaning as an onion: a multi-layered structure comprising (1) content analysis, (2) conceptual blending, and (3) pragmatic intentionality. This three-dimensional framework allows for a richer and more cognitively grounded approach to metaphor interpretation in computational systems. At the first level, metaphors are annotated through basic conceptual elements. At the second level, we model conceptual combinations, linking components to emergent meanings. Finally, at the third level, we introduce a pragmatic vocabulary to capture speaker intent, communicative function, and contextual effects, aligning metaphor understanding with pragmatic theories. By unifying these layers into a single formal framework, our model lays the groundwork for computational methods capable of representing metaphorical meaning beyond surface associations, toward deeper, more context-sensitive reasoning.

Related papers

• Human-like conceptual representations emerge from language prediction [72.5875173689788]
  Large language models (LLMs) trained exclusively through next-token prediction over language data exhibit remarkably human-like behaviors.<n>Are these models developing concepts akin to humans, and if so, how are such concepts represented and organized?<n>Our results demonstrate that LLMs can flexibly derive concepts from linguistic descriptions in relation to contextual cues about other concepts.<n>These findings establish that structured, human-like conceptual representations can naturally emerge from language prediction without real-world grounding.
  arXiv  Detail & Related papers  (2025-01-21T23:54:17Z)
• A Complexity-Based Theory of Compositionality [53.025566128892066]
  In AI, compositional representations can enable a powerful form of out-of-distribution generalization.<n>Here, we propose a definition, which we call representational compositionality, that accounts for and extends our intuitions about compositionality.<n>We show how it unifies disparate intuitions from across the literature in both AI and cognitive science.
  arXiv  Detail & Related papers  (2024-10-18T18:37:27Z)
• Science is Exploration: Computational Frontiers for Conceptual Metaphor Theory [0.0]
  We show that Large Language Models (LLMs) can accurately identify and explain the presence of conceptual metaphors in natural language data. Using a novel prompting technique based on metaphor annotation guidelines, we demonstrate that LLMs are a promising tool for large-scale computational research on conceptual metaphors.
  arXiv  Detail & Related papers  (2024-10-11T17:03:13Z)
• Compositional Entailment Learning for Hyperbolic Vision-Language Models [54.41927525264365]
  We show how to fully leverage the innate hierarchical nature of hyperbolic embeddings by looking beyond individual image-text pairs.<n>We propose Compositional Entailment Learning for hyperbolic vision-language models.<n> Empirical evaluation on a hyperbolic vision-language model trained with millions of image-text pairs shows that the proposed compositional learning approach outperforms conventional Euclidean CLIP learning.
  arXiv  Detail & Related papers  (2024-10-09T14:12:50Z)
• DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning [89.92601337474954]
  Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations. We introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding.
  arXiv  Detail & Related papers  (2023-06-15T10:41:23Z)
• MetaCLUE: Towards Comprehensive Visual Metaphors Research [43.604408485890275]
  We introduce MetaCLUE, a set of vision tasks on visual metaphor. We perform a comprehensive analysis of state-of-the-art models in vision and language based on our annotations. We hope this work provides a concrete step towards developing AI systems with human-like creative capabilities.
  arXiv  Detail & Related papers  (2022-12-19T22:41:46Z)
• Toward a Visual Concept Vocabulary for GAN Latent Space [74.12447538049537]
  This paper introduces a new method for building open-ended vocabularies of primitive visual concepts represented in a GAN's latent space. Our approach is built from three components: automatic identification of perceptually salient directions based on their layer selectivity; human annotation of these directions with free-form, compositional natural language descriptions. Experiments show that concepts learned with our approach are reliable and composable -- generalizing across classes, contexts, and observers.
  arXiv  Detail & Related papers  (2021-10-08T17:58:19Z)
• Metaphor Generation with Conceptual Mappings [58.61307123799594]
  We aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs. We propose to control the generation process by encoding conceptual mappings between cognitive domains. We show that the unsupervised CM-Lex model is competitive with recent deep learning metaphor generation systems.
  arXiv  Detail & Related papers  (2021-06-02T15:27:05Z)
• Towards Visual Semantics [17.1623244298824]
  We study how humans build mental representations, i.e., concepts, of what they visually perceive. In this paper we provide a theory and an algorithm which learns substance concepts which correspond to the concepts, that we call classification concepts. The experiments, though preliminary, show that the algorithm manages to acquire the notions of Genus and Differentia with reasonable accuracy.
  arXiv  Detail & Related papers  (2021-04-26T07:28:02Z)
• MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding [22.756157298168127]
  Based on a theoretically-grounded connection between metaphors and symbols, we propose a method to automatically construct a parallel corpus. For the generation task, we incorporate a metaphor discriminator to guide the decoding of a sequence to sequence model fine-tuned on our parallel data. A task-based evaluation shows that human-written poems enhanced with metaphors are preferred 68% of the time compared to poems without metaphors.
  arXiv  Detail & Related papers  (2021-03-11T16:39:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.