Discovering Conceptual Metaphors Across Topics and Media Types

• URL: http://arxiv.org/abs/2608.06652v1
• Date: Thu, 06 Aug 2026 23:43:08 GMT
• Title: Discovering Conceptual Metaphors Across Topics and Media Types
• Abstract summary: We present an unsupervised method that extracts linguistic metaphors from a corpus and uses a structured clustering approach to form groups corresponding to conceptual metaphors.<n>For example, left-leaning podcasts tend to conceptualize media stories as a weapon, while right-leaning sources commonly discuss the economy as a system subject to vertical changes.
• Score: 40.43680452279358
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conceptual metaphors guide our thinking and actions by allowing us to reason about more abstract experiences (e.g., paying taxes) in terms of more concrete or embodied experiences (e.g., carrying a physical load) (Lakoff and Johnson, 2011). It follows that different conceptual metaphors can result in different reasoning: framing paying taxes as an investment in a community rather than a physical load leads to a very different outlook on taxation. Identifying the conceptual metaphors guiding a speaker or writer thus helps to reveal their framing of events. Though these metaphors can't be observed directly, groups of linguistic metaphors, metaphorical expressions as they appear in language, serve as evidence for them. Motivated by this, we present an unsupervised method that extracts linguistic metaphors from a corpus and uses a structured clustering approach to form groups corresponding to conceptual metaphors. Using this method, we point to key topical and framing differences in left- vs. right-leaning podcasts. For example, left-leaning podcasts tend to conceptualize media stories as a weapon, while right-leaning sources commonly discuss the economy as a system subject to vertical changes.

Related papers

• Floating or Suggesting Ideas? A Large-Scale Contrastive Analysis of Metaphorical and Literal Verb-Object Constructions [53.690096725532726]
  We analyze 297 English verb-object pairs (e.g., float idea vs. suggest idea) in 2M corpus sentences.<n>Using five NLP tools, we extract 2,293 cognitive and linguistic features capturing affective, lexical, syntactic, and discourse-level properties.<n>Cross-pair results show literal contexts have higher lexical frequency, cohesion, and structural regularity, while metaphorical contexts show greater affective load, imageability, lexical diversity, and constructional specificity.
  arXiv  Detail & Related papers  (2026-04-09T14:08:57Z)
• IDIOLEX: Unified and Continuous Representations for Idiolectal and Stylistic Variation [88.98544786373212]
  Existing sentence representations primarily encode what a sentence says, rather than how it is expressed.<n>We develop sentence representations that capture style and dialect, decoupled from semantic content.<n>We introduce IDIOLEX, a framework for training models that combines supervision from a sentence's provenance with linguistic features of a sentence's content.
  arXiv  Detail & Related papers  (2026-04-06T14:17:24Z)
• Meanings are like Onions: a Layered Approach to Metaphor Processing [0.0]
  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.
  arXiv  Detail & Related papers  (2025-07-14T14:56:46Z)
• 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)
• CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation [35.14142183519002]
  This paper introduces a large-scale high quality annotated Chinese Metaphor Corpus, which comprises around 28K sentences. To ensure the accuracy and consistency of our annotations, we introduce a comprehensive set of guidelines. Breaking tradition, our approach to metaphor generation emphasizes grounds and their distinct features rather than the conventional combination of tenors and vehicles.
  arXiv  Detail & Related papers  (2024-02-20T17:00:41Z)
• LMs stand their Ground: Investigating the Effect of Embodiment in Figurative Language Interpretation by Language Models [0.0]
  Figurative language is a challenge for language models since its interpretation deviates from their conventional order and meaning. Yet, humans can easily understand and interpret metaphors as they can be derived from embodied metaphors. This study shows how larger language models perform better at interpreting metaphoric sentences when the action of the metaphorical sentence is more embodied.
  arXiv  Detail & Related papers  (2023-05-05T11:44:12Z)
• ConceptBeam: Concept Driven Target Speech Extraction [69.85003619274295]
  We propose a novel framework for target speech extraction based on semantic information, called ConceptBeam. In our scheme, a concept is encoded as a semantic embedding by mapping the concept specifier to a shared embedding space. We use it to bridge modality-dependent information, i.e., the speech segments in the mixture, and the specified, modality-independent concept.
  arXiv  Detail & Related papers  (2022-07-25T08:06:07Z)
• 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)
• 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)
• Metaphoric Paraphrase Generation [58.592750281138265]
  We use crowdsourcing to evaluate our results, as well as developing an automatic metric for evaluating metaphoric paraphrases. We show that while the lexical replacement baseline is capable of producing accurate paraphrases, they often lack metaphoricity. Our metaphor masking model excels in generating metaphoric sentences while performing nearly as well with regard to fluency and paraphrase quality.
  arXiv  Detail & Related papers  (2020-02-28T16:30:33Z)

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.