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Recurrent multiword units as networks

Sequentiality as basis for linguistic generalizations

Bibliographic Data

ID12197577
AuthorsAlvin Cheng-Hsien Chen (National Taiwan Normal University, corresponding author), Alvin Cheng‐Hsien Chen (0000-0003-0740-4633, Department of English , 34879 National Taiwan Normal University , Taipei , Taiwan)
Year2026
Volume37
Issue1
Pages63-104
Publication date2026-02-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueCognitive Linguistics (JOURNAL)
Journal identifiersISSN: 0936-5907 • E-ISSN: 1613-3641
PublisherDe Gruyter Mouton (PUBLISHER • DE)
DOI10.1515/cog-2025-0015
OpenAlexW7129017858
LanguageEN
References cited65

Recurrent multiword units (RMUs) are central to language processing, yet their systematic identification and networked organization remain understudied. This study combines corpus-based and network-analytic methods to examine how RMUs contribute to the emergence of constructional schemas. Drawing on a 185-million-word corpus of Taiwan Mandarin, we pursue two aims. First, we propose a quantitative method for identifying cohesive RMUs based on word predictability in context. Second, we model RMUs as a network in which nodes represent RMUs and edges encode structural and semantic similarity, estimated with a state-of-the-art large language model. A comparison with a random sequence network confirms the non-random structure of the RMU network. Analysis of its topology reveals exemplar-based semantic groupings that support higher-level generalizations. These findings highlight RMUs as key building blocks in linguistic categorization, where subgroupings emerge through sequential lexical associations that underlie the formation of grammatical patterns and hierarchical structure

Basis (linear algebra · ENCODE · Identification (biology · Predictability · Semantic network · Semantics (computer science · Word (group theory · Language and cultural evolution · Natural Language Processing Techniques · Neurobiology of Language and Bilingualism

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