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Who cites «Evaluating Historical Text Normalization Systems: How Well Do They Generalize?»

10 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

  1. Historical German Text Normalization Using Type- and Token-Based Language Modeling — 2024 · arXiv:2409.02841
  2. Normalizing without Modernizing: Keeping Historical Wordforms of Middle French while Reducing Spelling Variants — our card · 2024 · Findings of the Association for Computational Linguistics: NAACL 2024
  3. Multilingual Event Extraction from Historical Newspaper Adverts — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  4. Automatic Normalisation of Early Modern French — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
  5. Dealing with Abbreviations in the Slovenian Biographical Lexicon — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  6. Semi-supervised Contextual Historical Text Normalization — our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
  7. A Large-Scale Comparison of Historical Text Normalization Systems — our card · 2019 · 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)
  8. Few-Shot and Zero-Shot Learning for Historical Text Normalization — our card · 2019 · Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
  9. Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
  10. Imitation Learning for Neural Morphological String Transduction — 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing