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Using Statistical Models of Morphology in the Search for Optimal Units of Representation in the Human Mental Lexicon

机译：使用形态统计模型在人类心理词典中寻找最佳表示单位

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Determining optimal units of representing morphologically complex words in the mental lexicon is a central question in psycholinguistics. Here, we utilize advances in computational sciences to study human morphological processing using statistical models of morphology, particularly the unsupervised Morfessor model that works on the principle of optimization. The aim was to see what kind of model structure corresponds best to human word recognition costs for multimorphemic Finnish nouns: a model incorporating units resembling linguistically defined morphemes, a whole-word model, or a model that seeks for an optimal balance between these two extremes. Our results showed that human word recognition was predicted best by a combination of two models: a model that decomposes words at some morpheme boundaries while keeping others unsegmented and a whole-word model. The results support dual-route models that assume that both decomposed and full-form representations are utilized to optimally process complex words within the mental lexicon.
机译：确定表示心理词典中形态复杂单词的最佳单位是心理语言学的中心问题。在这里，我们利用计算机科学的先进性，使用形态统计模型，特别是基于优化原理的无监督Morfessor模型，来研究人类形态处理。目的是了解哪种模型结构最适合多态芬兰名词的人类单词识别成本：一个包含类似于语言定义的词素的单元的模型，一个全词模型或一个在这两个极端之间寻求最佳平衡的模型。我们的结果表明，通过两种模型的组合可以最好地预测人的单词识别：一种在某些词素边界分解单词而另一部分不分词的模型和全词模型。结果支持双路线模型，该模型假定分解和完整形式的表示都被用来优化处理心理词典中的复杂单词。

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