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?
Trigram models can be more powerful for document retrieval than unigram models, but if you want to handle spelling errors, they will not be of much help.  You need some form of fuzzy matching for that.

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.