Proposing a ranker for semantic error checker using context-sensitive features

Nowadays, a large volume of documents is generated daily. These documents generated by different persons, thus, the documents contain spelling errors. Therefore, existence of automatic writing assistance tools such as spell checker/corrector can help to improve their quality. Context-sensitive are misspelled words that have been wrongly converted into another word of the language. Thus, detection of real-word errors requires discourse analysis. In this paper, we propose a language independent discourse-aware discriminative ranker and use information of whole document and a log-linear model for ranking. To evaluate our method, we augment it into two context-sensitive spellchecker systems; one is based on Statistical Machine Translation (SMT) and the other is based on language model. For more evaluation, we also use two different tests. Proposed method causes outperform about 17 %over the SMT base approach with respect to detection and correction recall.