Normalization in text basically refers to standardization or canonicalization of tokens, which we derived from documents in the previous step. The simplest scenario possible could be the case where query tokens are an exact match to the list of tokens in document, however there can be cases when that is not true. The intent of normalization is to have the query and index terms in the same form. For instance, if you query U.K., you might also be expecting U.K.
Token normalization can be performed either by implicitly creating equivalence classes or by maintaining the relations between unnormalized tokens. There might be cases where we find superficial differences in character sequences of tokens, in such cases query and index term matching becomes difficult. Consider the words anti-disciplinary and anti-disciplinary. If both these words get mapped into one term named after one of the members of the set for example anti-disciplinary, text retrieval would become so efficient...
