A Large-Scale Comparison of Historical Text Normalization Systems

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Standard

A Large-Scale Comparison of Historical Text Normalization Systems. / Bollmann, Marcel.

Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, 2019. p. 3885-3898.

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

Harvard

Bollmann, M 2019, A Large-Scale Comparison of Historical Text Normalization Systems. in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, pp. 3885-3898, 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - NAACL-HLT 2019, Minneapolis, United States, 03/06/2019. https://doi.org/10.18653/v1/N19-1389

APA

Bollmann, M. (2019). A Large-Scale Comparison of Historical Text Normalization Systems. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 3885-3898). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1389

Vancouver

Bollmann M. A Large-Scale Comparison of Historical Text Normalization Systems. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics. 2019. p. 3885-3898 https://doi.org/10.18653/v1/N19-1389

Author

Bollmann, Marcel. / A Large-Scale Comparison of Historical Text Normalization Systems. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, 2019. pp. 3885-3898

Bibtex

@inproceedings{eb0e4063f7e4414597cfd6f107ea69ad,
title = "A Large-Scale Comparison of Historical Text Normalization Systems",
abstract = "There is no consensus on the state-of-the-art approach to historical text normalization. Many techniques have been proposed, including rule-based methods, distance metrics, character-based statistical machine translation, and neural encoder–decoder models, but studies have used different datasets, different evaluation methods, and have come to different conclusions. This paper presents the largest study of historical text normalization done so far. We critically survey the existing literature and report experiments on eight languages, comparing systems spanning all categories of proposed normalization techniques, analysing the effect of training data quantity, and using different evaluation methods. The datasets and scripts are made publicly available.",
author = "Marcel Bollmann",
year = "2019",
doi = "10.18653/v1/N19-1389",
language = "English",
pages = "3885--3898",
booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
publisher = "Association for Computational Linguistics",
note = "2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - NAACL-HLT 2019 ; Conference date: 03-06-2019 Through 07-06-2019",

}

RIS

TY - GEN

T1 - A Large-Scale Comparison of Historical Text Normalization Systems

AU - Bollmann, Marcel

PY - 2019

Y1 - 2019

N2 - There is no consensus on the state-of-the-art approach to historical text normalization. Many techniques have been proposed, including rule-based methods, distance metrics, character-based statistical machine translation, and neural encoder–decoder models, but studies have used different datasets, different evaluation methods, and have come to different conclusions. This paper presents the largest study of historical text normalization done so far. We critically survey the existing literature and report experiments on eight languages, comparing systems spanning all categories of proposed normalization techniques, analysing the effect of training data quantity, and using different evaluation methods. The datasets and scripts are made publicly available.

AB - There is no consensus on the state-of-the-art approach to historical text normalization. Many techniques have been proposed, including rule-based methods, distance metrics, character-based statistical machine translation, and neural encoder–decoder models, but studies have used different datasets, different evaluation methods, and have come to different conclusions. This paper presents the largest study of historical text normalization done so far. We critically survey the existing literature and report experiments on eight languages, comparing systems spanning all categories of proposed normalization techniques, analysing the effect of training data quantity, and using different evaluation methods. The datasets and scripts are made publicly available.

U2 - 10.18653/v1/N19-1389

DO - 10.18653/v1/N19-1389

M3 - Article in proceedings

SP - 3885

EP - 3898

BT - Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

PB - Association for Computational Linguistics

T2 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - NAACL-HLT 2019

Y2 - 3 June 2019 through 7 June 2019

ER -

ID: 239617830