Leveraging Known Semantics for Spelling Correction

Focusing on applications for analyzing learner language which evaluate semantic appropriateness and accuracy, we build from previous work which modeled some aspects of interaction, namely a picture description task (PDT), with the goal of integrating a spelling correction component in this context. After parsing a sentence and extracting semantic relations, a surprising number of analysis failures stem from misspellings, deviating from expected input in ways that can be modeled when the content of the interaction is known. We thus explore the use of spelling correction tools and language modeling to correct misspellings that often lead to errors in obtaining semantic forms, and we show that such tools can significantly reduce the number of unanalyzable cases. The work is useful for any context where image descriptions or some expected content is available, but not necessarily expected linguistic forms.