On the Diachronic Stability of Irregularity in Inflectional Morphology
Many languages' inflectional morphological systems are replete with irregulars, i.e., words that do not seem to follow standard inflectional rules. In this work, we quantitatively investigate the conditions under which irregulars can survive in a language over the course of time. Using recurrent neural networks to simulate language learners, we test the diachronic relation between frequency of words and their irregularity.
Code (0)
등록된 구현이 없습니다.
Tasks
RelationSimilar Papers 제목 키워드 기반
The natural stability of autonomous morphology
Autonomous morphology, such as inflection class systems and paradigmatic distribution patterns, is widespread and diachronically resilient in natural language. Why this should be so has remained unclear given that autono…
On the Complexity and Typology of Inflectional Morphological Systems
We quantify the linguistic complexity of different languages' morphological systems. We verify that there is an empirical trade-off between paradigm size and irregularity: a language's inflectional paradigms may be eithe…
Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge
How does knowledge of one language's morphology influence learning of inflection rules in a second one? In order to investigate this question in artificial neural network models, we perform experiments with a sequence-to…
Shortest Length Total Orders Do Not Minimize Irregularity in Vector-Valued Mathematical Morphology
Mathematical morphology is a theory concerned with non-linear operators for image processing and analysis. The underlying framework for mathematical morphology is a partially ordered set with well-defined supremum and in…
Morphologically Aware Word-Level Translation
We propose a novel morphologically aware probability model for bilingual lexicon induction, which jointly models lexeme translation and inflectional morphology in a structured way. Our model exploits the basic linguistic…
Bilingual Lexicon InductionTranslation