  Spelling irregularities, known now as spelling mistakes, have been found for
several centuries. As humans, we are able to understand most of the misspelled
words based on their location in the sentence, perceived pronunciation, and
context. Unlike humans, computer systems do not possess the convenient auto
complete functionality of which human brains are capable. While many programs
provide spelling correction functionality, many systems do not take context
into account. Moreover, Artificial Intelligence systems function in the way
they are trained on. With many current Natural Language Processing (NLP)
systems trained on grammatically correct text data, many are vulnerable against
adversarial examples, yet correctly spelled text processing is crucial for
learning. In this paper, we investigate how spelling errors can be corrected in
context, with a pre-trained language model BERT. We present two experiments,
based on BERT and the edit distance algorithm, for ranking and selecting
candidate corrections. The results of our experiments demonstrated that when
combined properly, contextual word embeddings of BERT and edit distance are
capable of effectively correcting spelling errors.
