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

Craig Messner, Thomas Lippincott


Abstract
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.
Anthology ID:
2024.latechclfl-1.26
Volume:
Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)
Month:
March
Year:
2024
Address:
St. Julians, Malta
Editors:
Yuri Bizzoni, Stefania Degaetano-Ortlieb, Anna Kazantseva, Stan Szpakowicz
Venues:
LaTeCHCLfL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
264–269
Language:
URL:
https://aclanthology.org/2024.latechclfl-1.26
DOI:
Bibkey:
Cite (ACL):
Craig Messner and Thomas Lippincott. 2024. Pairing Orthographically Variant Literary Words to Standard Equivalents Using Neural Edit Distance Models. In Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024), pages 264–269, St. Julians, Malta. Association for Computational Linguistics.
Cite (Informal):
Pairing Orthographically Variant Literary Words to Standard Equivalents Using Neural Edit Distance Models (Messner & Lippincott, LaTeCHCLfL-WS 2024)
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PDF:
https://aclanthology.org/2024.latechclfl-1.26.pdf
Supplementary material:
 2024.latechclfl-1.26.SupplementaryMaterial.zip
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 https://aclanthology.org/2024.latechclfl-1.26.mp4