Modeling Morphology With Linear Discriminative Learning: Considerations and Design Choices.
German nouns
Widrow-Hoff learning
frequency of occurrence
linear discriminative learning
multivariate multiple regression
semantic roles
semi-productivity
wug task
Journal
Frontiers in psychology
ISSN: 1664-1078
Titre abrégé: Front Psychol
Pays: Switzerland
ID NLM: 101550902
Informations de publication
Date de publication:
2021
2021
Historique:
received:
04
06
2021
accepted:
04
10
2021
entrez:
6
12
2021
pubmed:
7
12
2021
medline:
7
12
2021
Statut:
epublish
Résumé
This study addresses a series of methodological questions that arise when modeling inflectional morphology with Linear Discriminative Learning. Taking the semi-productive German noun system as example, we illustrate how decisions made about the representation of form and meaning influence model performance. We clarify that for modeling frequency effects in learning, it is essential to make use of incremental learning rather than the end-state of learning. We also discuss how the model can be set up to approximate the learning of inflected words in context. In addition, we illustrate how in this approach the wug task can be modeled. The model provides an excellent memory for known words, but appropriately shows more limited performance for unseen data, in line with the semi-productivity of German noun inflection and generalization performance of native German speakers.
Identifiants
pubmed: 34867600
doi: 10.3389/fpsyg.2021.720713
pmc: PMC8634146
doi:
Types de publication
Journal Article
Langues
eng
Pagination
720713Informations de copyright
Copyright © 2021 Heitmeier, Chuang and Baayen.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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