Robust Word Recognition Via Semi-Character Recurrent Neural Network
The Cambridge University effect from the psycholinguistics literature has demonstrated a robust word processing mechanism in humans, where jumbled words (e.g. Cmabrigde /Cambridge) are recognized with little cost.
Inspired by the findings from the Cambrigde University effect, we propose a word recognition model based on a semi-character level recursive neural network (scRNN). In our experiments, we demonstrate that scRNN has significantly more robust performance in word spelling correction (i.e. word recognition) compared to existing spelling checkers. Furthermore, we demonstrate that the model is cognitively plausible by replicating a psycholinguistics experiment about human reading difficulty using our model.
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