Conceptual Networks for Cross-Linguistic Idiomatic Expressions: A Feature-Based Graph Approach

Jul 16, 2026

Presents an interpretable network-based framework to represent idiomatic and figurative meaning across eight typologically diverse languages covering 160 conventional expressions, mostly idioms. Each expression is annotated with binary conceptual features from cognitive-linguistic theory (e.g., containment, concealment, emotional and social), and pairwise Jaccard similarities form a weighted graph. Community detection groups idioms by conceptual schema rather than language, matching cognitive predictions. The resulting conceptual network is more informative than distributional embeddings, scales via LLM-based annotation, supports idiom detection improvements, and enables cross-lingual transfer using conceptual proximity.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

Abstract

Introduction

Related Work

Cognitive Linguistics and Idioms

Computational Models of Idioms

Conceptual Networks for Cross‑Linguistic Idiomatic Expressions: A Feature‑Based Graph Approach Kiran Pala∗ University of Eastern Finland, Finland [kiran.pala@uef.fi](kiran.pala@uef.fi) Punam Silu Lixun Yu Indian Institute of Technology Ropar, India University of Eastern Finland, Finland [punam.24hsz0007@iitrpr.ac.in](punam.24hsz0007@iitrpr.ac.in) [luxinyu@uef.fi](luxinyu@uef.fi) arXiv :2607 .09576v 1 [ cs .CL] 10 Jul 2026 Abstract We present an interpretable network-based framework for representing idiomatic and figurative meaning across eight typologically diverse languages, totaling 160 conventional expressions, the large majority of which are idiomatic. Each expression is annotated with binary conceptual features (containment, concealment, emotional, social, etc.) derived from cognitivelinguistic theory, and pairwise Jaccard similarities define a weighted graph. Community detection reveals that idioms cluster by conceptual schema rather than by language, producing a structure consistent with cognitive-linguistic predictions. The conceptual network captures unique semantic information not present in distributional embeddings, can be scaled via automatic annotation with LLMs, improves downstream idiom detection, and remains robust when enriched with corpus frequencies. Cross-lingual transfer experiments show that conceptual proximity alone can identify acceptable translation equivalents across five language families, with substantial gains over embedding-based baselines. Ablation studies demonstrate that all three feature dimensions—schemas, roles, and valence—contribute non-redundantly to both the network’s organizational properties and its performance on idiom detection, and that specific graph-derived signals (community membership, neighbor similarity) are particularly informative. The framework offers an interpretable, cross-linguistically stable representation of idiomatic meaning, combining theoretical grounding with practical utility. 1 Introduction Idioms such as spill the beans or kick the bucket pose a fundamental challenge for semantic rep- ∗ Corresponding author. resentation because their figurative meaning cannot be reliably derived from literal constituents. Cognitive linguistics has shown that many idioms are motivated by a small set of image schemas—e.g. , containment, concealment—that recur across typologically diverse languages (Kövecses, 2005) . However, the degree of conceptual overlap between idioms is graded rather than all-or-none, and modeling this graded structure can both advance theoretical understanding and improve natural language processing applications that require handling of non-literal language. Interpretable representations are particularly valuable for NLP tasks where model decisions must be understood and trusted. By grounding representation in cognitive theory, our framework offers both interpretability and crosslingual stability, complementing black‑box embedding models. Most computational models of idioms rely on surface lexical forms through distributional semantics or contextual embeddings (Fazly et al. , 2009) . While powerful, such approaches often miss the deep conceptual structure that underlies idiomaticity across languages, and they provide little insight into the semantic organisation of idioms. Furthermore, existing studies predominantly focus on a small number of well-resourced languages, neglecting the typological diversity that could reveal universal aspects of idiom organisation. Large language models can occasionally identify idioms, but they do not offer a structured conceptual space in which idiomatic meaning can be systematically compared across languages. We introduce a network-based model of 160 idioms from eight languages representing five language families: Indo-European (English, Hindi, Bagri, Spanish), Uralic (Finnish), Japonic (Japanese), Sino-Tibetan (Chinese), and Dravidian (Malayalam) . This selection spans agglutinative, fus

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What does the community detection analysis reveal?

Community detection shows that idioms cluster by conceptual schema rather than by language. This organization aligns with cognitive-linguistic predictions and supports cross-linguistic stability of idiomatic meaning.

Jul 16, 2026

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