A computational study of cross-situational techniques for learning word-to-meaning mappings
A computational study of cross-situational techniques for learning word-to-meaning mappings
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DOI：
10.1016/s0010-0277(96)00728-7
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发表时间：
1996-10-01
期刊：
影响因子：
3.4
通讯作者：
Siskind, JM
中科院分区：
文献类型：
--
作者：
Siskind, JM
本文提出了一个计算研究的一部分，词汇习得任务所面临的儿童，即收购的词到意义的映射。它首先近似这个任务作为一个正式的数学问题。然后，它提出了一个实现的算法来解决这个问题，说明其操作上的一个小例子。这种算法提供了一个精确的解释的直观概念的跨情境学习和原则的对比应用在话语中的话。它鲁棒地学习一个homemarks词典，尽管嘈杂的多词输入，在参考不确定性的存在下，没有特定于正在学习的语言的先验知识。计算模拟证明了该算法的鲁棒性，并说明了如何算法的基础上跨情境学习和对比的原则可能能够解决词汇习得问题的大小所面临的儿童，弱，最坏情况下的假设的类型和数量的数据。
This paper presents a computational study of part of the lexical-acquisition task faced by children, namely the acquisition of word-to-meaning mappings. It first approximates this task as a formal mathematical problem. It then presents an implemented algorithm for solving this problem, illustrating its operation on a small example. This algorithm offers one precise interpretation of the intuitive notions of cross-situational learning and the principle of contrast applied between words in an utterance. It robustly learns a homonymous lexicon despite noisy multi-word input, in the presence of referential uncertainty, with no prior knowledge that is specific to the language being learned. Computational simulations demonstrate the robustness of this algorithm and illustrate how algorithms based on cross-situational learning and the principle of contrast might be able to solve lexical-acquisition problems of the size faced by children, under weak, worst-case assumptions about the type and quantity of data available.