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Inferring Inflection Classes with Description Length

机译:用描述长度推断变形等级

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We discuss the notion of an inflection class system , a traditional ingredient of the description of inflection systems of nontrivial complexity. We distinguish systems of microclasses , which partition a set of lexemes in classes with identical behavior, and systems of macroclasses , which group lexemes that are similar enough in a few larger classes. On the basis of the intuition that macroclasses should contribute to a concise description of the system, we propose one algorithmic method for inferring macroclasses from raw inflectional paradigms, based on minimisation of the description length of the system under a given strategy for identifying morphological alternations in paradigms. We then exhibit classifications produced by our implementation on French and European Portuguese conjugation data, and argue that they constitute an appropriate systematisation of traditional classifications. To arrive at such a concincing systematisation, it is crucial though that we use a local approach to class similarity (based on pairwise comparisons of paradigm cells) rather than a global approach (based on simultaneous comparison of all cells). We conclude that it is indeed possible to infer inflectional macroclasses objectively.
机译:我们讨论了拐点类系统的概念,这是描述非平凡复杂性的拐点系统的传统组成部分。我们区分了微类系统和宏类系统,其中微类系统将一组词素划分为具有相同行为的类,而宏类系统则将词素组合成几个较大类中足够相似的类。基于宏类应有助于简化系统描述的直觉,我们提出了一种算法方法,该方法基于原始拐点范式推导宏类,该方法基于最小化系统描述长度,在给定策略下可识别系统中的形态学变化。范例。然后,我们展示由我们的实现对法国和欧洲葡萄牙共轭数据产生的分类,并认为它们构成了传统分类的适当系统化。为了实现这种简洁的系统化,至关重要的是,尽管我们使用局部方法对类进行相似性(基于范式单元的成对比较),而不是全局方法(基于所有单元的同时比较)。我们得出结论,确实有可能客观地推断出屈折宏类。

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