A COMPUTATIONAL LEARNING-MODEL FOR METRICAL PHONOLOGY
A COMPUTATIONAL LEARNING-MODEL FOR METRICAL PHONOLOGY
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DOI：
10.1016/0010-0277(90)90042-i
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发表时间：
1990-02-01
期刊：
影响因子：
3.4
通讯作者：
KAYE, JD
中科院分区：
文献类型：
--
作者：
DRESHER, BE;KAYE, JD
语言学理论面临的主要挑战之一是解决所谓的“投射问题”。简而言之，语言学必须考虑这样一个事实：从一个既不系统又相对较小的数据库开始，一个人类儿童能够构建一种语法，这种语法在所有意图和目的上都反映了成人系统。在这篇文章中，我们将致力于语音结构的一个假设子系统的可学习性问题：重音系统。我们将描述一个计算机程序，它被设计用来获取语言结构的这个子部分。我们的方法遵循Chomsky（1981 a，B）的“原则和参数”模型。从计算的角度和学习理论的发展来看，这个模型特别有趣。我们对普遍语法（UG）的相关方面进行编码-这些方面的语言结构被认为是天生的，因此存在于每个语言系统中。学习过程包括确定一些参数，这些参数已经被证明是重音系统的基础，原则上，这些参数应该引导学习者对系统的假设，从该系统中可以获得主要的语言数据（即，对学习者的输入）被绘制。我们继续探讨这个学习系统的某些形式和实质性的属性。文章中提出并讨论了诸如跨参数依赖性、确定性、子集以及增量学习与一次性学习的问题。本研究提出的问题提供了另一个角度的压力系统的形式结构和一般的参数系统的可学习性。
One of the major challenges to linguistic theory is the solution of what has been termed the "projection problem". Simply put, linguistics must account for the fact that starting from a data base that is both unsystematic and relatively small, a human child is capable of constructing a grammar that mirrors, for all intents and purposes, the adult system. In this article we shall address ourselves to the question of the learnability of a postulated subsystem of phonological structure: the stress system. We shall describe a computer program which is designed to acquire this subpart of linguistic structure. Our approach follows the "principles and parameters" model of Chomsky (1981a, b). This model is particularly intersting from both a computational point of view and with respect to the development of learning theories. We encode the relevant aspects of universal grammar (UG)-those aspects of linguistic structure that are presumed innate and thus present in every linguistic system. The learning process consists of fixing a number of parameters which have been shown to underlie stress systems and which should, in principle, lead the learner to the postulation of the system from which the primary linguistic data (i.e., the input to the learner) is drawn. We go on to explore certain formal and substantive properties of this learning system. Questions such as cross-parameter dependencies, determinism, subsets, and incremental versus all-at-once learning are raised and discussed in the article. The issues raised by this study provide another perspective on the formal structure of stress systems and the learnability of parameter systems in general.