Abstract
Spellcheckers/predictors can help people in writing more efficiently. It is a well-known fact, for example, that spellcheckers/predictors can ease writing for people with dyslexia. However, most of the spellcheckers assume that wrong words contain just few errors (the literature claims that 80% to 95% of spelling errors contain one error), in terms of the four classical edit operation (i.e., addition, deletion, transposition, substitution), and that errors are isolated (i.e., each error involves just one word). In addition, since standard spellcheckers do not use context, they are not able to correct real-word errors. Finally, they usually are not predictors. This feature is very useful for people with dyslexia, as it allows them to type less characters. The aim of my research is to address the aspect of adaptation and personalization to the individual behavior for the model and the user interface of spellchecker/predictor, considering people with dyslexia. Specifically, we designed and trained a model that takes into account the typical errors (even real-word errors) made by people with dyslexia and the context for spellchecking and prediction, and the experiments to carry out for evaluating its performance. In addition, we formalized the parameters for making the interface adaptive, so that the user interaction with the system is light. In the next months, we will finish the development of the adaptive user interface. Then we will conduct experimental studies for testing the system. From a broader perspective, we try to generalize the system to other user types.
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Quattrini Li, A. (2013). An Adaptive Spellchecker and Predictor for People with Dyslexia. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_51
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DOI: https://doi.org/10.1007/978-3-642-38844-6_51
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