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Modeling morphology with Linear Discriminative Learning: considerations and design choices
Modeling morphology with Linear Discriminative Learning: considerations and design choices
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 endstate 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 in considerable detail. In general, 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.
38 pages, 5 figures, 10 tables; acknowledgements added
- University of Tübingen Germany
ACM Computing Classification System: InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
multivariate multiple regression, FOS: Computer and information sciences, linear discriminative learning, Widrow-Hoff learning, frequency of occurrence, German nouns, BF1-990, semi-productivity, semantic roles, wug task, Psychology, Computation and Language (cs.CL), General Psychology, Original Research
multivariate multiple regression, FOS: Computer and information sciences, linear discriminative learning, Widrow-Hoff learning, frequency of occurrence, German nouns, BF1-990, semi-productivity, semantic roles, wug task, Psychology, Computation and Language (cs.CL), General Psychology, Original Research
ACM Computing Classification System: InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Popularity provided by BIP!- University of Tübingen Germany
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 endstate 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 in considerable detail. In general, 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.
38 pages, 5 figures, 10 tables; acknowledgements added