用神经网络模拟人类学习动词变位类别的能力
Learning inflection classes using Adaptive Resonance Theory
- 采用自适应共振理论模型进行无监督聚类
- 在拉丁语、葡萄牙语等语言中取得最佳分类效果
- 结果可解释且适合研究语言演变
变位类别是语言学家使用的抽象概念，能描述语言中的规律性模式，为推断未见过的词形提供类比基础，是形态习得与处理的重要部分。本文通过无监督聚类方法，研究个体语言使用者对动词变位类别的学习能力。采用具有可调泛化程度（警觉参数）的自适应共振理论神经网络作为认知上合理的可解释模型，应用于拉丁语、葡萄牙语和爱沙尼亚语。聚类结果与已知变位类别相似度因变位系统复杂度而异，最佳性能出现在泛化参数的狭窄区间内。模型提取的特征与语言学对变位类别的描述高度一致。该模型未来可用于基于代理的语言演变研究。
原文摘要 · Abstract (English)
The concept of inflection classes is an abstraction used by linguists, and provides a means to describe patterns in languages that give an analogical base for deducing previously unencountered forms. This ability is an important part of morphological acquisition and processing. We study the learnability of a system of verbal inflection classes by the individual language user by performing unsupervised clustering of lexemes into inflection classes. As a cognitively plausible and interpretable computational model, we use Adaptive Resonance Theory, a neural network with a parameter that determines the degree of generalisation (vigilance). The model is applied to Latin, Portuguese and Estonian. The similarity of clustering to attested inflection classes varies depending on the complexity of the inflectional system. We find the best performance in a narrow region of the generalisation parameter. The learned features extracted from the model show similarity with linguistic descriptions of the inflection classes. The proposed model could be used to study change in inflection classes in the future, by including it in an agent-based model.
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