Is FrameNet usage-based? Predicting frame element coreness with information theory and gradient boosting

Datos Bibliográficos

ID17811488
AutoresVladimir Buskin (0009-0005-5824-1012, Catholic University of Eichstätt-Ingolstadt, autor de correspondencia)
Año2026
Volumen18
Fecha de publicación2026-01-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaLanguage and Cognition (JOURNAL)
Identificadores de la revistaISSN: 1866-9808 • E-ISSN: 1866-9859
EditorialCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/langcog.2026.10076
OpenAlexW7157225000
IdiomaEN
Referencias citadas44

The FrameNet project is a large-scale frame-semantic database with a seemingly usage-based core: It draws on 200,000 annotated sentences from representative corpora and offers the most comprehensive description of semantic valency patterns in English to date. Nevertheless, its empirical validity is weakened by the lack of statistical information on the distribution of lexical units, frames and frame elements. Similarly, the characterisation of frame elements as core, core-unexpressed, peripheral or extra-thematic – intended to indicate their essentiality to a frame – is primarily motivated on theoretical grounds. This raises the question of whether these labels are consistent with actual language use. After exhaustively extracting frequency data from Python’s NLTK FrameNet Corpus for all attested combinations of verbs, frames and frame elements, hierarchical gradient boosting models were trained on information-theoretic measures and word embeddings to predict the coreness of frame elements. The models provide strong usage-based evidence for a general core versus non-core distinction but cast doubt on further subdivisions such as core versus core-unexpressed or peripheral versus extra-thematic. While further validation is necessary, this contribution offers the first statistical perspective on the current state of FrameNet and its compatibility with usage-based approaches

Computational linguistics · FrameNet · Semantic property · Valency · Authorship Attribution and Profiling · Natural Language Processing Techniques · Neurobiology of Language and Bilingualism