Instead of training ngram probabilities on sentences (i.e., sequences of words), we can train them on words, wherein each word is a sequence of morphemes and morpheme boundaries.
to continue with the English example lighting, the bigram-MLE of the candidate segmentation *light=ing would be calculated as follows: Since the bigram 'X ing' is far more frequent than '# ing', P (#lightXing#) will receive a higher MLE than P (#light#ing#).