# Pairing Orthographically Variant Literary Words to Standard Equivalents Using Neural Edit Distance Models

arXiv: https://arxiv.org/abs/2401.15068 · HTML: https://mlsanity.com/paper/2401.15068/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Builds on (most cited first, 3 of 3)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [From Hero to Z'eroe: A Benchmark of Low-Level Adversarial Attacks](https://mlsanity.com/paper/2010.05648/) (2020)
- [Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics?](https://mlsanity.com/paper/1804.05416/) (2018)

## Built on by (most cited first, 1 of 1)

- [Examining Language Modeling Assumptions Using an Annotated Literary Dialect Corpus](https://mlsanity.com/paper/2410.02674/) (2024)

## Abstract (arXiv)

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.