提出统一计算理论解释多种语言的名词形态标记规律
Rethinking and formalising the state across languages: a unified computational learning theory account
- 基于模板的模块化认知框架，用集合函数建模状态机制
- 通过有限集运算学习算法预测语法配置，适用于多种合成语言
- 揭示状态与格、一致等语法依赖的共性，拓展对名词结构的理解
传统上，'状态'概念仅限于亚非语系的附着态，被视为语言特异性构词句法现象。本文主张状态是系统性、上下文依赖的构词句法机制，可跨合成语言选择语法模板。在以里夫方言为主要实证基础的模板化模块认知框架下，该理论为历来独立分析的多样名词标记模式提供了统一解释，并形式化为符号计算模型，其中状态由作用于语法模板的集合值函数表示。基于有限集运算的学习算法可习得并预测状态依赖的语法结构。该框架不仅涵盖名词形态，还对名词结构与词汇认知理论有更广泛影响，尤其提供了一种确定词-名词结构的统一分析。结果表明，状态是更广泛语法条件依赖关系的一个实例，还包括一致性和格。
原文摘要 · Abstract (English)
The linguistic notion of state has traditionally been restricted to the construct (annexation) state of Afroasiatic languages and treated as a language-specific morphosyntactic phenomenon. This article argues instead that the state is a systemic, context-dependent morphosyntactic mechanism that selects grammatical templates across synthetic languages. Within the Template-Based Modular Cognitive framework, taking Riffian as its primary empirical basis, the proposed theory provides a unified explanation for diverse nominal marking patterns traditionally analysed independently and is formalised as a symbolic computational model in which the state is represented by a set-valued function over grammatical templates. A learning algorithm based on finite-set operations acquires and predicts state-dependent grammatical configurations. Beyond nominal morphology, the framework has broader implications for theories of nominal structure and lexical cognition, in particular offering a unified analysis of determiner-noun structure. These results suggest that the state constitutes one instance of a broader class of syntactically conditioned dependencies that also includes agreement and grammatical case.
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