Discriminative Learning and the Lexicon: NDL and LDL

Yu-Ying Chuang, R. Baayen
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引用次数: 8

Abstract

Naive discriminative learning (NDL) and linear discriminative learning (LDL) are simple computational algorithms for lexical learning and lexical processing. Both NDL and LDL assume that learning is discriminative, driven by prediction error, and that it is this error that calibrates the association strength between input and output representations. Both words’ forms and their meanings are represented by numeric vectors, and mappings between forms and meanings are set up. For comprehension, form vectors predict meaning vectors. For production, meaning vectors map onto form vectors. These mappings can be learned incrementally, approximating how children learn the words of their language. Alternatively, optimal mappings representing the end state of learning can be estimated. The NDL and LDL algorithms are incorporated in a computational theory of the mental lexicon, the ‘discriminative lexicon’. The model shows good performance both with respect to production and comprehension accuracy, and for predicting aspects of lexical processing, including morphological processing, across a wide range of experiments. Since, mathematically, NDL and LDL implement multivariate multiple regression, the ‘discriminative lexicon’ provides a cognitively motivated statistical modeling approach to lexical processing.
辨别性学习与词汇:NDL和LDL
朴素判别学习(NDL)和线性判别学习(LDL)是用于词汇学习和词汇处理的简单计算算法。NDL和LDL都假设学习是判别性的,由预测误差驱动,并且正是这种误差校准了输入和输出表示之间的关联强度。两个词的形式和意义都用数字向量表示,并建立了形式和意义之间的映射关系。为了便于理解,形式向量预测意义向量。对于生产,意义向量映射到形式向量。这些映射可以循序渐进地学习,近似于儿童学习语言单词的方式。或者,可以估计代表学习最终状态的最优映射。NDL和LDL算法被整合到心理词典的计算理论中,即“判别词典”。在广泛的实验中,该模型在生成和理解精度以及预测词汇处理(包括词法处理)方面都显示出良好的性能。由于在数学上,NDL和LDL实现了多元回归,因此“判别词典”为词汇处理提供了一种认知驱动的统计建模方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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