---
title: "Who cites Synthetic Data for English Lexical…"
description: "The papers of the Elamur Research library that cite «Synthetic Data for English Lexical Normalization: How Close Can We Get to Manually Annotated Data?»…"
canonical: "https://research.elamur.ai/free/en/papers/dekker-2020-synthetic-data-english-lexical-normalization/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/dekker-2020-synthetic-data-english-lexical-normalization/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Synthetic Data for English Lexical Normalization…](https://md.research.elamur.ai/free/en/papers/dekker-2020-synthetic-data-english-lexical-normalization/) / cited by

# Who cites «Synthetic Data for English Lexical Normalization: How Close Can We Get to Manually Annotated Data?»

11 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/dekker-2020-synthetic-data-english-lexical-normalization/)

1. [LLM-Based Synthetic Datasets: Applications and Limitations in Toxicity Detection](https://aclanthology.org/2024.trac-1.6/) *— 2024 · Proceedings of the Fourth Workshop on Threat, Aggression & Cyberbullying @ LREC-COLING-2024*
2. [Making Sentence Embeddings Robust to User-Generated Content](https://aclanthology.org/2024.lrec-main.958/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
3. [Étude sur la normalisation lexicale de contenus produits par les utilisateurs A Study on the Lexical Normalisation of User-Generated Content](https://aclanthology.org/2023.tal-2.2/) *— 2024*
4. [Script Normalization for Unconventional Writing of Under-Resourced Languages in Bilingual Communities](https://aclanthology.org/2023.acl-long.809/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [MS@IW at SemEval-2022 Task 4: Patronising and Condescending Language Detection with Synthetically Generated Data](https://aclanthology.org/2022.semeval-1.47/) *— 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
6. [Tackling Irony Detection using Ensemble Classifiers](https://aclanthology.org/2022.lrec-1.754/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
7. [A Text Editing Approach to Joint Japanese Word Segmentation, POS Tagging, and Lexical Normalization](https://aclanthology.org/2021.wnut-1.9/) *— 2021 · Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)*
8. [Sequence-to-Sequence Lexical Normalization with Multilingual Transformers](https://aclanthology.org/2021.wnut-1.53/) *— 2021 · Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)*
9. [Structured abbreviation expansion in context](https://aclanthology.org/2021.findings-emnlp.85/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
10. [Universität Regensburg MaxS at GermEval 2021 Task 1: Synthetic Data in Toxic Comment Classification](https://aclanthology.org/2021.germeval-1.9/) *— 2021 · Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments*
11. [User-Generated Text Corpus for Evaluating Japanese Morphological Analysis and Lexical Normalization](https://aclanthology.org/2021.naacl-main.438/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*