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.

翻译：隐喻在日常语言中无处不在，使说话者能够通过具体领域表达抽象概念。尽管已有研究从认知和 Psycholinguistic 角度对隐喻进行了探讨，但将其与字面语言进行大规模比较的工作仍然有限，特别是对于近义表达。我们分析了约200万语料库句子中的297对英语动词-宾语对（例如，“float idea” 与 “suggest idea”），考察其语境用法。利用五种 NLP 工具，我们提取了 2,293 个认知和语言特征，涵盖情感、词汇、句法和篇章层面属性。我们探讨：(i) 隐喻性和字面性语境的特征是否存在差异（跨对分析），以及 (ii) 个别动词-宾语对内部是否存在分歧（对内分析）。跨对分析显示，字面性语境具有更高的词汇频率、连贯性和结构规律性，而隐喻性语境则表现出更大的情感负荷、可想象性、词汇多样性和构式特异性。对内分析揭示了显著的异质性，大多数对呈现非一致效应。这些结果表明，不存在单一、一致的分布模式能将隐喻用法与字面用法区分开来；相反，差异在很大程度上是构式特定的。总体而言，结合多样化特征的大规模数据为理解动词-宾语用法中隐喻与字面的对比提供了精细化的视角。