تقرير
Modeling morphology with Linear Discriminative Learning: considerations and design choices
| العنوان: | Modeling morphology with Linear Discriminative Learning: considerations and design choices |
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| المؤلفون: | Heitmeier, Maria, Chuang, Yu-Ying, Baayen, R. Harald |
| المصدر: | Frontiers in Psychology 12 (2021), p. 4929 |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language |
| الوصف: | 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 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.3389/fpsyg.2021.720713 |
| URL الوصول: | http://arxiv.org/abs/2106.07936 |
| رقم الأكسشن: | edsarx.2106.07936 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.3389/fpsyg.2021.720713 |
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