A neural network architecture for learning word-referent associations in multiple contexts
A neural network architecture for learning word-referent associations in multiple contexts
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DOI：
10.1016/j.neunet.2019.05.017
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发表时间：
2019-09-01
期刊：
影响因子：
7.8
通讯作者：
Araujo, Aluizio F. R.
中科院分区：
文献类型：
--
作者：
Bassani, Hansenclever F.;Araujo, Aluizio F. R.
本文提出了一个生物启发的神经计算架构，学习在不同的上下文中的单词和所指对象之间的关联，考虑从心理语言学和神经语言学的文献收集的证据。多层架构将对象的原始图像作为输入该方法利用自组织映射（Self-Organizing Map）建立新的关联节点，将词的指称对象（references）和音素流（labels）建立适当的表示，识别当前上下文，并将labels与指称对象（references）逐步关联根据需要，调整现有原型以更好地表示输入刺激，并删除过时/未使用的原型。该模型考虑了当前上下文，以检索具有多个含义的单词的正确含义。仿真结果表明，该模型可以达到高达78%的词所指联想的准确性在歧义的情况下，以及近似的学习率的人类报告的三个不同的作者在五个跨情境的单词学习实验，也显示出类似的学习模式，在不同的学习条件。(C)2019爱思唯尔有限公司版权所有。
This article proposes a biologically inspired neurocomputational architecture which learns associations between words and referents in different contexts, considering evidence collected from the literature of Psycholinguistics and Neurolinguistics. The multi-layered architecture takes as input raw images of objects (referents) and streams of word's phonemes (labels), builds an adequate representation, recognizes the current context, and associates label with referents incrementally, by employing a Self-Organizing Map which creates new association nodes (prototypes) as required, adjusts the existing prototypes to better represent the input stimuli and removes prototypes that become obsolete/unused. The model takes into account the current context to retrieve the correct meaning of words with multiple meanings. Simulations show that the model can reach up to 78% of word-referent association accuracy in ambiguous situations and approximates well the learning rates of humans as reported by three different authors in five Cross-Situational Word Learning experiments, also displaying similar learning patterns in the different learning conditions. (C) 2019 Elsevier Ltd. All rights reserved.