Memory-based grammatical correction

Abstract

We describe the ’TILB’ team entry for the CONLL-2013 Shared Task. Our system consists of five memory-based classifiers that generate correction suggestions for center positions in small text windows of two words to the left and to the right. Trained on the Google Web 1T corpus, the first two classifiers determine the presence of a determiner or a preposition between all words in a text. The second pair of classifiers determine which is the most likely correction of an occurring determiner or preposition. The fifth classifier is a general word predictor which is used to suggest noun and verb form corrections. We report on the scores attained and errors corrected and missed. We point out a number of obvious improvements to boost the scores obtained by the system.