使用人工和自然语言语料库建模位置不变的正字法处理

Previous research indicates that readers may construct a location-invariant letter string representation before associating it with a lexical representation. In neural network modeling, this is achieved with a two-stack model in which a hidden layer first learns to map location-specific letter representations into word-centered positions. Then, another stack is trained to map them into lexical representations. However, the model has been validated only in one language (French). In this study, we replicated the model with a Finnish language corpus having substantially more resembling words (KIS