A Large-Scale Comparison of Historical Text Normalization Systems
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- OA-A Large-Scale Comparison of Historical Text Normalization Systems
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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.
Original language | English |
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Title of host publication | 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 |
Publication date | 2019 |
Pages | 3885-3898 |
DOIs | |
Publication status | Published - 2019 |
Event | 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - NAACL-HLT 2019 - Minneapolis, United States Duration: 3 Jun 2019 → 7 Jun 2019 |
Conference
Conference | 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - NAACL-HLT 2019 |
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Land | United States |
By | Minneapolis |
Periode | 03/06/2019 → 07/06/2019 |
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