A Self-Organizing Neural Network Model of Lexical and Morphological Acquisition

人类语言学习的一个重要方面是学习者将现有模式概括为新实例的能力。泛化问题是当前语言习得机制争论的焦点。在过去的十年里，许多研究者以英语过去时态的习得为例，争论语言习得应该被看作是一个符号的、基于规则的学习过程，还是一个联结主义的、统计的学习过程。然而，这场争论的大部分都围绕着一组特定的联结主义模型，即作为语言习得模型的反向传播网络。反向传播算法，特别是在语言习得的背景下，现在有几个局限性。在这项研究中，我们探讨了自组织神经网络，特别是自组织特征映射作为语言习得的模型。与反向传播相反，自组织网络使用无监督学习，不需要监督者或明确的教师;学习完全通过系统响应输入环境的自组织来实现。此外，多个自组织网络可以通过Hebbian学习连接，这是一种生物学激励的共现学习机制。我们的项目首先涉及到基于自组织和Hebbian学习原理的语言习得联结模型的发展。它还涉及到词汇和形态学习得的建模，关于结构化词汇表征的出现以及概括和表征之间的关系。这些研究应该使我们能够确定：（1）结构化的词汇表征是否以及如何从学习的自组织过程中产生，而不是天生可用;（2）泛化在多大程度上是新表征的函数;（3）自组织过程是否以及如何导致从泛化错误中恢复。我们认为，自组织和赫布学习提供了必要的计算和心理语言学机制的词汇表征，形态泛化，并在语言习得的恢复。我们的研究将从一个新的角度整合以往的实证和建模结果，为词汇和词法的习得研究提供一个新的理论视角和方法论工具。

A crucial aspect of human language learning is the learner's ability to generalize existing patterns to novel instances. The issue of generalization is a focal point of current debates on mechanisms of language acquisition. In the last ten years, many researchers have used the acquisition of the English past tense as an example to debate whether language acquisition should be viewed as a symbolic, rule-based learning process or as a connectionist, statistical learning process. However, most of this debate has revolved around a specific cluster of connectionist models, the back-propagation network as a model of language acquisition. The back-propagation algorithm, especially in the context of language acquisition, has several limitations now. In this study, we explore self-organizing neural networks, in particular, the self-organizing feature maps as models of language acquisition. In contrast to back-propagation, the self-organizing network uses unsupervised learning that requires no presence of a supervisor or an explicit teacher; learning is achieved entirely by the system's self-organization in response to the input environment. Moreover, multiple self-organizing networks can be connected via Hebbian learning, a biologically motivated co-occurrence learning mechanism. Our project involves first the development of a connectionist model of language acquisition based on principles of self-organization and Hebbian learning. It further involves the modeling of the acquisition of the lexicon and morphology, with respect to the emergence of structured lexical representations and the relationship between generalization and representation. These studies should allow us to determine (1) whether and how structured lexical representations can emerge from self-organizing processes of learning, rather than being available innately; (2) the extent to which generalization is a function of the new representation; and (3) whether and how self-organizing processes lead to the recovery from generalization errors. We propose that self-organization and Hebbian learning provide the necessary computational and psycholinguistic mechanisms for lexical representation, morphological generalization, and recovery in language acquisition. Our project will integrate previous empirical and modeling results in a new light, and offer a new theoretical perspective as well as a methodological tool for the study of the acquisition of the lexicon and the morphology.