Incremental Bayesian Learning of Semantic Categories
Incremental Bayesian Learning of Semantic Categories
复制标题
语义类别的增量贝叶斯学习
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Mirella Lapata
中科院分区：
文献类型：
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作者：
Lea Frermann;Mirella Lapata
类别学习的模型在认知科学中得到了广泛的研究，主要是在知觉抽象或人工刺激上进行测试。在本文中，我们专注于从自然语言刺激中获得的类别，即单词（例如，椅子属于家具类）。我们提出了一个贝叶斯模型，与以前的工作不同，它在一个过程中学习两个类别及其特征。我们的模型采用粒子滤波器，一个连续的蒙特卡罗方法通常用于近似概率推理的增量设置。与最先进的基于图的方法的比较表明，我们的模型在质量上学习了更好的类别，并在学习过程中展示了认知的合理性。
Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper we focus on categories acquired from natural language stimuli, that is words (e.g., chair is a member of the FURNITURE category). We present a Bayesian model which, unlike previous work, learns both categories and their features in a single process. Our model employs particle filters, a sequential Monte Carlo method commonly used for approximate probabilistic inference in an incremental setting. Comparison against a state-of-the-art graph-based approach reveals that our model learns qualitatively better categories and demonstrates cognitive plausibility during learning.
允许婴儿对动物物种进行分类的感知线索。
DOI：
10.1006/jecp.1996.0047
发表时间：
1996
期刊：
Journal of experimental child psychology.
影响因子：
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作者：
Quinn,PC;Eimas,PD
通讯作者：
Eimas,PD