Pairing Orthographically Variant Literary Words to Standard Equivalents
  Using Neural Edit Distance Models
Craig Messner Tom Lippincott
We present a novel corpus consisting of orthographically variant words found
in works of 19th century U.S. literature annotated with their corresponding
"standard" word pair. We train a set of neural edit distance models to pair
these variants with their standard forms, and compare the performance of these
models to the performance of a set of neural edit distance models trained on a
corpus of orthographic errors made by L2 English learners. Finally, we analyze
the relative performance of these models in the light of different negative
training sample generation strategies, and offer concluding remarks on the
unique challenge literary orthographic variation poses to string pairing
methodologies.