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Compositional Distributional Semantics and the Conjunction Effect in Language Models

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Yoshihiro Maruyama
Arisa Yasuda
Published in
Multi-disciplinary Trends in Artificial Intelligence
Publisher
Springer Nature Singapore

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Abstract

This chapter delves into the conjunction effect, a cognitive phenomenon where people tend to judge a conjunctive statement as more probable than one of its constituent statements, as demonstrated in the Linda problem. The author presents a compositional account of the effect using the DisCoCat model, which interprets grammatical types as vector spaces and reductions as linear maps. The chapter explores how adjectives, modeled as linear maps, can reallocate semantic mass across dimensions, and how noun-noun compounds can be combined using the Frobenius merge. The author also discusses the role of off-diagonal structure in adjectives and provides a systematic prediction and ablation of the conjunction effect. The chapter concludes with a discussion of the implications of these findings for cognitive science and natural language processing.
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next chapter Quantum Patches for Efficient Learning
Title
Compositional Distributional Semantics and the Conjunction Effect in Language Models
Authors
Yoshihiro Maruyama
Arisa Yasuda
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Multi-disciplinary Trends in Artificial Intelligence

Print ISBN: 978-981-9549-56-6

Electronic ISBN: 978-981-9549-57-3

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-4957-3_7
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