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Modelling Analogies and Analogical Reasoning: Connecting Cognitive Science Theory and NLP Research

Paper · 2026 · Molly R. Petersen, Claire E. Stevenson and Lonneke van der Plas

Summary

This survey links cognitive-science accounts of analogy with work in NLP. The authors argue that NLP systems tend to rely on entity-level resemblance and pay too little attention to shared relational structure. They do not introduce new data, models or benchmarks. Instead, they outline human analogical reasoning as five interacting processes and use Gentner's Structural Mapping Theory as the main reference point. They then describe different kinds of analogies and connect recent analogy-solving research to these processes. The review is limited to text-based analogies and to methods built on neural word embeddings.

The authors observe that experiments on particular analogy types usually focus on narrow aspects, mostly entity-level pairs and the mapping step. They also show how broader NLP problems can be viewed as analogy problems, including cross-domain generalization, spurious correlations, low-resource domains and annotation. The paper's value is a theory-grounded starting point for NLP researchers who want to model relational understanding and not only surface similarity.

Key points

  • The paper describes five processes of analogical reasoning, illustrated with planet:sun::electron:nucleus: RETRIEVAL, MAPPING, REREPRESENTATION, ABSTRACTION and ENCODING. MAPPING is treated as the core process.
  • Structural Mapping Theory (Gentner, 1983) separates three cases by attribute and relational similarity: literal similarity, mere appearance and analogy. Holyoak and Thagard add three soft mapping pressures: isomorphism, semantic similarity and pragmatic centrality.
  • In humans, near analogies are easier to solve than far ones, but far analogies do more to support novel problem solving and relational thinking.
  • Examples of NLP tasks reframed as analogies:
    • sentence generation treated as sentence analogy (Wang and Lepage, 2020);
    • NLI entailment and negation recast as analogy problems (Wijesiriwardene et al.);
    • ARAIDA, an analogy-based method for interactive data annotation (Huang et al., 2024).
  • Stated limitations: the coverage of cognitive science is only partial, and the review leaves out lifespan development, knowledge graphs and relation embeddings. Published in TACL vol. 14 (2026), pp. 711–732.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of AmsterdamAmsterdam · Netherlands144 works · 173,906 citations🎓École Polytechnique Fédérale de LausanneLausanne · Switzerland44 works · 7,050 citations🎓Università della Svizzera italianaLugano · Switzerland6 works · 647 citations🔬Dalle Molle Institute for Artificial Intelligence ResearchLugano · Switzerland5 works · 18 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksPsychology · 1,552 worksExperimental and Cognitive Psychology · 870 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,426 works

Cite

Cite as Molly R. Petersen, Claire E. Stevenson and Lonneke van der Plas. 2026. Modelling Analogies and Analogical Reasoning: Connecting Cognitive Science Theory and NLP Research. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl.a.632

@article{petersen2026modelling,
  title = {{Modelling Analogies and Analogical Reasoning: Connecting Cognitive Science Theory and NLP Research}},
  author = {Molly R. Petersen, Claire E. Stevenson and Lonneke {van der Plas}},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2026},
  doi = {10.1162/tacl.a.632},
  url = {https://doi.org/10.1162/tacl.a.632}
}

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