Grammatically incorrect sentences result either from an unknown (possibly misspelled) word, an incorrect word order or even an omitted / redundant word. Sentences with these errors are a bottle-neck to NLP systems because they cannot be parsed correctly. Human beings are able to overcome this problem (either occurring in spoken or written language) since they are capable of doing a semantic similarity search to find out if a similar utterance has been heard before or a syntactic similarity search for a stored utterance that shares structural similarities with the input. If the syntactic and semantic analysis of the rest of the input can be done correctly, then a ‘gap’ that exists in the utterance, can be uniquely identified. In this paper, a system named SAUCOLA which is based on a concept lattice, that mimics human skills in resolving knowledge gaps that exist in written language is presented. The preliminary results show that correct stored sentences can be retrieved based on the words contained in the incorrect input sentence.