论文标题
在NEN中建模言语形态
Modelling Verbal Morphology in Nen
论文作者
论文摘要
Nen的言语形态非常复杂。传递动词最多可以占据1,740个独特的形式。具有较大组合空间和低资源设置的组合效果增加了对NLP工具的需求。 NEN的形态利用分布式指数 - 将形式映射到含义的非平凡手段。在本文中，我们试图使用最先进的机器学习模型来对NEN语言形态进行建模，以重新构成形态学。我们探索并分类这些系统产生的错误类型。我们的结果表明对训练数据组成的敏感性；动词类型的不同分布产生不同的精度（具有电子复杂性的图案）。我们还通过合成案例研究可以从训练数据中推断出的模式类型。
Nen verbal morphology is remarkably complex; a transitive verb can take up to 1,740 unique forms. The combined effect of having a large combinatoric space and a low-resource setting amplifies the need for NLP tools. Nen morphology utilises distributed exponence - a non-trivial means of mapping form to meaning. In this paper, we attempt to model Nen verbal morphology using state-of-the-art machine learning models for morphological reinflection. We explore and categorise the types of errors these systems generate. Our results show sensitivity to training data composition; different distributions of verb type yield different accuracies (patterning with E-complexity). We also demonstrate the types of patterns that can be inferred from the training data through the case study of syncretism.
