Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddings

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Letters From the Past : Modeling Historical Sound Change Through Diachronic Character Embeddings. / Boldsen, Sidsel; Paggio, Patrizia.

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2022. p. 6713–6722.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Boldsen, S & Paggio, P 2022, Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddings. in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, pp. 6713–6722, 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland, 23/05/2022. <https://aclanthology.org/2022.acl-long.463>

APA

Boldsen, S., & Paggio, P. (2022). Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddings. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 6713–6722). Association for Computational Linguistics. https://aclanthology.org/2022.acl-long.463

Vancouver

Boldsen S, Paggio P. Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddings. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics. 2022. p. 6713–6722

Author

Boldsen, Sidsel ; Paggio, Patrizia. / Letters From the Past : Modeling Historical Sound Change Through Diachronic Character Embeddings. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2022. pp. 6713–6722

Bibtex

@inproceedings{56a99a037afa478c8cd3a33d16f84adb,
title = "Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddings",
abstract = "While a great deal of work has been done on NLP approaches to lexical semantic change detection, other aspects of language change have received less attention from the NLP community. In this paper, we address the detection of sound change through historical spelling. We propose that a sound change can be captured by comparing the relative distance through time between the distributions of the characters involved before and after the change has taken place. We model these distributions using PPMI character embeddings. We verify this hypothesis in synthetic data and then test the method{\textquoteright}s ability to trace the well-known historical change of lenition of plosives in Danish historical sources. We show that the models are able to identify several of the changes under consideration and to uncover meaningful contexts in which they appeared. The methodology has the potential to contribute to the study of open questions such as the relative chronology of sound shifts and their geographical distribution.",
author = "Sidsel Boldsen and Patrizia Paggio",
year = "2022",
month = may,
language = "English",
pages = "6713–6722",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
publisher = "Association for Computational Linguistics",
note = " 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022 ; Conference date: 23-05-2022 Through 25-05-2022",

}

RIS

TY - GEN

T1 - Letters From the Past

T2 - 60th Annual Meeting of the Association for Computational Linguistics

AU - Boldsen, Sidsel

AU - Paggio, Patrizia

PY - 2022/5

Y1 - 2022/5

N2 - While a great deal of work has been done on NLP approaches to lexical semantic change detection, other aspects of language change have received less attention from the NLP community. In this paper, we address the detection of sound change through historical spelling. We propose that a sound change can be captured by comparing the relative distance through time between the distributions of the characters involved before and after the change has taken place. We model these distributions using PPMI character embeddings. We verify this hypothesis in synthetic data and then test the method’s ability to trace the well-known historical change of lenition of plosives in Danish historical sources. We show that the models are able to identify several of the changes under consideration and to uncover meaningful contexts in which they appeared. The methodology has the potential to contribute to the study of open questions such as the relative chronology of sound shifts and their geographical distribution.

AB - While a great deal of work has been done on NLP approaches to lexical semantic change detection, other aspects of language change have received less attention from the NLP community. In this paper, we address the detection of sound change through historical spelling. We propose that a sound change can be captured by comparing the relative distance through time between the distributions of the characters involved before and after the change has taken place. We model these distributions using PPMI character embeddings. We verify this hypothesis in synthetic data and then test the method’s ability to trace the well-known historical change of lenition of plosives in Danish historical sources. We show that the models are able to identify several of the changes under consideration and to uncover meaningful contexts in which they appeared. The methodology has the potential to contribute to the study of open questions such as the relative chronology of sound shifts and their geographical distribution.

M3 - Article in proceedings

SP - 6713

EP - 6722

BT - Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

PB - Association for Computational Linguistics

Y2 - 23 May 2022 through 25 May 2022

ER -

ID: 307749151