The pluralization palette

Unveiling semantic clusters in English nominal pluralization through distributional semantics

Dados Bibliográficos

ID4580803
AutoresElnaz Shafaei-Bajestan (0000-0002-1441-5608, University of Tübingen, autor correspondente), Masoumeh Moradipour-Tari (0000-0002-3739-5347, University of Tübingen), Peter Uhrig (0000-0003-1237-7414, Bayer (Germany)), R Harald Baayen (0000-0003-3178-3944, University of Tübingen)
Ano2024
Volume34
Fascículo4
Páginas369-413
Data de publicação2024-12-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoMorphology (JOURNAL)
Identificadores do periódicoISSN: 1871-5621 • E-ISSN: 1871-5656
EditoraSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11525-024-09428-9
PMID39540059
OpenAlexW4400586038
IdiomaEN
Citações recebidas5
Referências citadas77

Using distributional semantics, we show that English nominal pluralization exhibits semantic clusters. For instance, the change in semantic space from singulars to plurals differs depending on whether a word denotes, e.g., a fruit, or an animal. Languages with extensive noun classes such as Swahili and Kiowa distinguish between these kind of words in their morphology. In English, even though not marked morphologically, plural semantics actually also varies by semantic class. A semantically informed method, CosClassAvg, is introduced that is compared to two other methods, one implementing a fixed shift from singular to plural, and one creating plural vectors from singular vectors using a linear mapping (FRACSS). Compared to FRACSS, CosClassAvg predicted plural vectors that were more similar to the corpus-extracted plural vectors in terms of vector length, but somewhat less similar in terms of orientation. Both FRACSS and CosClassAvg outperform the method using a fixed shift vector to create plural vectors, which does not do justice to the intricacies of English plural semantics. A computational modeling study revealed that the observed difference between the plural semantics generated by these three methods carries over to how well a computational model of the listener can understand previously unencountered plural forms. Among all methods, CosClassAvg provides a good balance for the trade-off between productivity (being able to understand novel plural forms) and faithfulness to corpus-extracted plural vectors (i.e., understanding the particulars of the meaning of a given plural form

Linguistics · Palette (painting · Programming language · Semantics (computer science · Sociology · Computer Science · Linguistic Variation and Morphology · Mathematics · Natural Language Processing Techniques · Philosophy · Syntax, Semantics, Linguistic Variation