# Typing error handling n-gram character index and vector space model

Suppose I apply tri-gram indexing for my document collection, and is implementing a vector-space model to help retrieving the document. In the text it is mentioned implementing a trigram will introduce a new step in filtering the result. However, what are the problems that I need to be aware of if I implement tfidf/vector-space model? The reason I am exploring this option is to try handling basic spelling error handling, does it really work in practice?

For example the string, "I like dosg too" would fool a unigram model because "dosg" is likely "dogs" misspelled, and it will encode it as "dosg" : 1. But you have the same problem in a trigram model. It will encode "I like dosg" : 1, "like dosg too" : 1. Which is not really better, as it will still not match any trigrams with the word "dogs" in it.
• argh, i meant character n-gram, for instance "I like dosg" becomes [" I ", " li", "lik", "ike", "ke ", " do", "dos", "osg", "sg "] – Jeffrey04 Nov 9 '15 at 1:49