  Language processing mechanism by humans is generally more robust than
computers. The Cmabrigde Uinervtisy (Cambridge University) effect from the
psycholinguistics literature has demonstrated such a robust word processing
mechanism, where jumbled words (e.g. Cmabrigde / Cambridge) are recognized with
little cost. On the other hand, computational models for word recognition (e.g.
spelling checkers) perform poorly on data with such noise. Inspired by the
findings from the Cmabrigde Uinervtisy effect, we propose a word recognition
model based on a semi-character level recurrent 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 and character-based convolutional neural network.
Furthermore, we demonstrate that the model is cognitively plausible by
replicating a psycholinguistics experiment about human reading difficulty using
our model.
