Floating or Suggesting Ideas? A Large-Scale Contrastive Analysis of Metaphorical and Literal Verb-Object Constructions

Prisca Piccirilli; Alexander Fraser; Sabine Schulte im Walde

cs.CLApr 9, 2026

Floating or Suggesting Ideas? A Large-Scale Contrastive Analysis of Metaphorical and Literal Verb-Object Constructions

Authors: Prisca Piccirilli, Alexander Fraser, Sabine Schulte im Walde

Organizations: Institute for Natural Language Processing (IMS), University of Stuttgart, Germany · Technical University of Munich (TUM), Germany · Munich Center for Machine Learning (MCML), Germany

Abstract

Metaphor pervades everyday language, allowing speakers to express abstract concepts via concrete domains. While prior work has studied metaphors cognitively and psycholinguistically, large-scale comparisons with literal language remain limited, especially for near-synonymous expressions. We analyze 297 English verb-object pairs (e.g., float idea vs. suggest idea) in ~2M corpus sentences, examining their contextual usage. Using five NLP tools, we extract 2,293 cognitive and linguistic features capturing affective, lexical, syntactic, and discourse-level properties. We address: (i) whether features differ between metaphorical and literal contexts (cross-pair analysis), and (ii) whether individual VO pairs diverge internally (within-pair analysis). 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. Within-pair analyses reveal substantial heterogeneity, with most pairs showing non-uniform effects. These results suggest no single, consistent distributional pattern that distinguishes metaphorical from literal usage. Instead, differences are largely construction-specific. Overall, large-scale data combined with diverse features provides a fine-grained understanding of metaphor-literal contrasts in VO usage.

Figures & tables

Figure 1: Pipeline of our approach: based on an existing set of metaphorival vs. literal VO pairs, we extract sentences containing these VOs. We then run five NLP tools on all sentences (masking the VO, see Section 3 ) to extract more than 2,200 cognitive and linguistic features in order to analyze the contrast between metaphorical and literal language usage from (i) a cross-pair and (ii) a within-pair perspective.

Figure 2: Metaphorical vs. literal VO preferences against total frequencies on a logarithmic scale, with points color-coded by dominance (green for literal-dominant, orange for metaphorical-dominant). The ratio ranges from 0 (entirely literal usage) to 1 (entirely metaphorical usage). Labels indicate the dominant VO followed by its corresponding counterpart in parenthesis, For example, met. VO breathe life (lit. VO instill life ) is metaphorical dominant with a ratio of 1.0. For better readability, we report in Table 2 the 10 most (i) frequency-balanced, (ii) literal-dominant and (iii) metaphor-dominant VO pairs.

Figure 3: Metaphorical vs. literal verb-only preferences (independently of their direct objects) against total frequencies on a logarithmic scale, with points color-coded by dominance (green for literal-dominant, orange for metaphorical-dominant). The ratio ranges from 0 (entirely literal usage) to 1 (entirely metaphorical usage). Labels indicate the dominant verb followed by its corresponding counterpart in parenthesis.

Category	Count
Total number of sentences	2,058,787
Sentences with literal VO s	1,724,892
Sentences with metaphorical VO s	333,895
Total number of VO pairs	297
VO pairs literal > metaphorical	222
VO pairs metaphorical > literal	75

Table 1: Distribution of literal and metaphorical sentences across VO pairs in the dataset. The table reports the total number of sentences per category and the number of VO pairs for which one usage type predominates.

Metaphorical	Literal	Met Count	Lit Count	Ratio	Log 2
Top 10 frequency-balanced VO Pairs
absorb concept	assimilate concept	89	65	0.58	0.45
absorb experience	assimilate experience	109	91	0.55	0.26
frame idea	pose idea	147	120	0.55	0.29
mount production	organise production	322	305	0.51	0.07
mount production	organize production	322	316	0.51	0.03

Table 2: Top VO pairs across distributional profiles: (i) the 10 most frequency-balanced pairs, (ii) the 10 pairs where metaphorical usage is more frequent than their literal counterparts, and (iii) the 10 pairs where literal usage is more frequent. Met Count and Lit Count indicate the number of sentences in which the metaphorical and literal VO occur, respectively. Ratio corresponds to the proportion of metaphorical usage (cf. Figure 2 ). Log 2 represents the base-2 logarithm of the ratio between metaphorical and literal frequencies, capturing the direction and magnitude of the imbalance.

Panel of Figure 4 — Figure 4: Tool-level aggregation of distributional dominance for 93 clearly-distinctive VO pairs (mean absolute difference $\geq 1$ standard deviations in at least three tools, detailed methodology explained in Sections 3 and 4.3 ). Feature-level contrasts were collapsed into tool-level directional judgments (literal-dominant in green vs. metaphor-dominant in orange) for VO pairs meeting the distinctiveness criterion in at least three tools. VO pairs are sorted top to bottom from mostly literal-dominant to mostly metaphor-dominant. For example, the VO pair instill idea vs. breathe idea behaves significantly more "metaphorical-like" across features of four tools (SEANCE, TAACO, TAALES and TAASSC), i.e., the metaphorical variant breathe idea introduces robust distributional differences detectable across linguistic features (structurally, lexically, discourse-wise, etc.) Sections 3 and 4.2 describe the linguistic aspects captures by the tools.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Metaphorical VO	Literal VO	Met Count	Lit Count
absorb knowledge	assimilate knowledge	354	190
absorb information	assimilate information	1,154	511
absorb idea	assimilate idea	369	141
absorb culture	assimilate culture	308	127
absorb material	assimilate material	382	85
absorb lesson	assimilate lesson	366	50

Appendix

Table 3: Metaphorical vs. Literal Verb–Object Pairs with Corpus Frequencies (N=297)

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MetFuse: Figurative Fusion between Metonymy and Metaphor

Saptarshi Ghosh, Tianyu Jiang

University of Cincinnati

Figurative Language Understanding

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Metonymy and metaphor often co-occur in natural language, yet computational work has studied them largely in isolation. We introduce a framework that transforms a literal sentence into three figurative variants: metonymic, metaphoric, and hybrid. Using this framework, we construct MetFuse, the first dedicated dataset of figurative fusion between metonymy and metaphor, containing 1,000 human-verified meaning-aligned quadruplets totaling 4,000 sentences. Extrinsic experiments on eight existing benchmarks show that augmenting training data with MetFuse consistently improves both metonymy and metaphor classification, with hybrid examples yielding the largest gains on metonymy tasks. Using this dataset, we also analyze how the presence of one figurative type impacts another. Our findings show that both human annotators and large language models better identify metonymy in hybrid sentences than in metonymy-only sentences, demonstrating that the presence of a metaphor makes a metonymic noun more explicit. Our dataset is publicly available at: https://github.com/cincynlp/MetFuse.

Abstract

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