An Information Retrieval Approach to Spelling Suggestion

摘要

In this paper, we present a two-step language-independent spelling suggestion system. In the first step, candidate suggestions are generated using an Information Retrieval(IR) approach. In step two, candidate suggestions are re-ranked using a new string similarity measure that uses the length of the longest common substrings occurring at the beginning and end of the words. We obtained very impressive results by reranking candidate suggestions using the new similarity measure. The accuracy of first suggestion is 92.3%, 90.0% and 83.5% for Dutch, Danish and Bulgarian language datasets respectively.