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Modeling Morphology With Linear Discriminative Learning: Considerations and Design Choices
Copyright policy )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.
Comment: 38 pages, 5 figures, 10 tables; acknowledgements added
Microsoft Academic Graph classification: Context (language use) Representation (mathematics) Semantic role labeling Noun Artificial intelligence business.industry business Natural language processing computer.software_genre computer Generalization Set (psychology) Inflection German language.human_language language Psychology
ACM Computing Classification System: InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
General Psychology, German nouns, linear discriminative learning, semi-productivity, multivariate multiple regression, Widrow-Hoff learning, frequency of occurrence, Psychology, BF1-990, Original Research, semantic roles, wug task, Computation and Language (cs.CL), FOS: Computer and information sciences, Computer Science - Computation and Language
General Psychology, German nouns, linear discriminative learning, semi-productivity, multivariate multiple regression, Widrow-Hoff learning, frequency of occurrence, Psychology, BF1-990, Original Research, semantic roles, wug task, Computation and Language (cs.CL), FOS: Computer and information sciences, Computer Science - Computation and Language
Microsoft Academic Graph classification: Context (language use) Representation (mathematics) Semantic role labeling Noun Artificial intelligence business.industry business Natural language processing computer.software_genre computer Generalization Set (psychology) Inflection German language.human_language language Psychology
ACM Computing Classification System: InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
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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.
Comment: 38 pages, 5 figures, 10 tables; acknowledgements added