用线性判别学习建模形态学：考虑和设计选择
Modeling morphology with Linear Discriminative Learning: considerations and design choices
摘要：
这项研究解决了一系列的方法问题时出现的建模曲折形态与线性判别学习。以半生产性德语名词系统为例，我们说明了如何决定的形式和意义的表示影响模型的性能。我们澄清，在学习中的频率效应建模，这是必不可少的，利用增量学习，而不是学习的最终状态。我们还讨论了如何建立模型来近似上下文中的屈折词的学习。此外，我们说明了如何在这种方法中的wug任务可以建模相当详细。一般来说，该模型提供了一个很好的记忆已知的单词，但适当地显示出更多的有限的性能看不见的数据，符合德国名词的变形和泛化性能的母语为德语的半生产力。
英文摘要：
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.
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