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# Who cites «A Lexicon-Based Approach for Detecting Hedges in Informal Text»

7 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/islam-2020-lexicon-based-approach-detecting-hedges/)

1. [Who Speaks for Whom? LLM-Generated Survey Data as a Proxy for Public Opinion](https://aclanthology.org/2026.nlpcss-1.9/) *— 2026 · Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science*
2. [A Reality Check on Context Utilisation for Retrieval-Augmented Generation](https://aclanthology.org/2025.acl-long.968/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [Investigating Subjective Factors of Argument Strength: Storytelling, Emotions, and Hedging](https://aclanthology.org/2025.argmining-1.12/) *— 2025 · Proceedings of the 12th Argument mining Workshop*
4. [Racial Bias Trends in the Text of US Legal Opinions](https://arxiv.org/abs/2307.01693) *— 2023 · arXiv:2307.01693*
5. [How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs?](https://aclanthology.org/2021.emnlp-main.28/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
6. haque-2023-emotion-enriched-psycholinguistics-features-based
7. nikolaev-2023-adverbs-surprisingly
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