| Challenge: | Existing negation detection methods in English are not available. |
| Approach: | They propose to annotate a Dutch dialogue corpus with negation cues and their scopes. |
| Outcome: | The proposed method can detect negation cues and scope in Dutch dialogues with high precision and recall. |
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| Challenge: | Negation is an important characteristic of language, and a major component of information extraction from text. |
| Approach: | They propose to use a popular transfer learning model to solve Negation Detection and Scope Resolution tasks in 3 datasets that have gained popularity over the years. |
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| Challenge: | Negation is a common linguistic feature that is crucial in many language understanding tasks. |
| Approach: | They propose a new approach to detect negation in language models using data augmentation and negation masking. |
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Detecting Negation Cues and Scopes in Spanish (2020.lrec-1)
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Salud María Jiménez-Zafra, Roser Morante, Eduardo Blanco, María Teresa Martín Valdivia, L. Alfonso Ureña López
| Challenge: | Negation is a phenomenon that "relates an expression e to another expression with a meaning that is in some way opposed to the meaning of e" previous work on negation in English has focused mostly and only recently on annotation tasks. |
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| Challenge: | Negations are key to determining sentence meaning, making them essential for logical reasoning. |
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| Challenge: | Negation is a core construction in natural language, but state-of-the-art pre-trained language models often handle it incorrectly. |
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| Challenge: | Negation is a contextual phenomenon that needs to be addressed in sentiment analysis. |
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Making Language Models Robust Against Negation (2025.naacl-long)
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| Challenge: | Negation is a semantic phenomenon that alters an expression to convey the opposite meaning. |
| Approach: | They propose a self-supervised method to make language models more robust against negation by pre-training models. |
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To Learn or Not to Learn: Replaced Token Detection for Learning the Meaning of Negation (2024.lrec-main)
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| Challenge: | State-of-the-art language models perform well on a variety of language tasks, but struggle with understanding negation cues in tasks like natural language inference (NLI). |
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| Challenge: | Using annotator-generated examples, one can evaluate systems with synthetic language that is not representative of language in the wild. |
| Approach: | They analyze negation in eight popular corpora spanning six natural language understanding tasks. |
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Assessing Monotonicity Reasoning in Dutch through Natural Language Inference (2023.findings-eacl)
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| Challenge: | a novel dataset for natural language inference (NLI) is used to study monotonicity reasoning in Dutch. |
| Approach: | They investigate monotonicity reasoning in Dutch using a novel dataset . they find that models struggle with downward entailing contexts . |
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