Who cites «Synthetic Data for English Lexical Normalization: How Close Can We Get to Manually Annotated Data?»
- LLM-Based Synthetic Datasets: Applications and Limitations in Toxicity Detection — 2024 · Proceedings of the Fourth Workshop on Threat, Aggression & Cyberbullying @ LREC-COLING-2024
- Making Sentence Embeddings Robust to User-Generated Content — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Script Normalization for Unconventional Writing of Under-Resourced Languages in Bilingual Communities — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Tackling Irony Detection using Ensemble Classifiers — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Sequence-to-Sequence Lexical Normalization with Multilingual Transformers — 2021 · Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
- Structured abbreviation expansion in context — 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021
- User-Generated Text Corpus for Evaluating Japanese Morphological Analysis and Lexical Normalization — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- higashiyama-2021-text-editing-approach-joint-japanese
- meyer-2022-ms-iw-semeval-2022-task
- nishimwe-2024-etude-sur-la-normalisation-lexicale
- schmidhuber-2021-universitat-regensburg-maxs-germeval-2021
This page in Markdown → https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dekker-2020-synthetic-data-english-lexical-normalization/