String inputs in Machine Learning
One of the popular models is the Bag of Words model

Also, you can model the words as integers.. they have 'relative distance metrics' for that, and capture the very essence of the classification process. However a downside to that is the preprocessing step is expensive and also you need to have some domain knowledge. 

A pretty famous distance metric is the Levenshtein distance which is based on the the number of single character edits. eg. that is, $d(walk,talk)&lt;d(walk,plod)$. 

The metrics depend upon the context of the classification process.. for example your distance metric for classifying rhyming words will be different from those designed to classify synonyms/words conveying similar meanings.For a list of string metrics, take a look at this wikipedia article.

Also, you can take a look at this review paper.