Papers with negation detection
Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish. (2021.naacl-srw)
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| Challenge: | Negation resolution remains an acute and continuously researched question in Natural Language Processing. |
| Approach: | They propose to use multilingual pre-trained general representation models to detect negation scope in languages without annotated data. |
| Outcome: | The proposed model achieves token-level F1 score between English, Spanish, French, and Russian. |
Negation Detection in Dutch Spoken Human-Computer Conversations (2022.lrec-1)
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| 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. |
Joint Entity Extraction and Assertion Detection for Clinical Text (P19-1)
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| Challenge: | Existing systems for in-formation extraction treat negative medical findings as a pipeline of two separate tasks. |
| Approach: | They propose a multi-task neural model to jointly extract entities and negations from medical reports. |
| Outcome: | The proposed model performs considerably better than existing systems on a 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset. |
Improving Feature Extraction for Pathology Reports with Precise Negation Scope Detection (C18-1)
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| Challenge: | a broad coverage, linguistically precise English resource grammar detects negation scope in sentences taken from pathology reports. |
| Approach: | They use a linguistically precise English resource grammar to detect negation scope in pathology reports. |
| Outcome: | The proposed approach improves classification of cancer reports with respect to laterality compared with NegEx. |
Improving negation detection with negation-focused pre-training (2022.naacl-main)
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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. |
| Outcome: | The proposed approach improves negation detection performance and generalizability over the strong baseline NegBERT. |
Negation Scope Refinement via Boundary Shift Loss (2023.findings-acl)
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| Challenge: | Negation detection is a complex linguistic phenomenon with long spans . existing methods tend to make wrong predictions around the scope boundaries . |
| Approach: | They propose a model which engages the Boundary Shift Loss to refine the predicted boundary. |
| Outcome: | The proposed model refines the predicted boundary on multiple datasets. |
FReND: A French Resource of Negation Data (2024.lrec-main)
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| Challenge: | Negation data are limited by the language models of the BERT-generation, which are still underperforming on tasks and benchmarks featuring negation. |
| Approach: | FReND is a freely available corpus of French language in which negations are hand-annotated by their cues and scopes. |
| Outcome: | FReND is the largest dataset available for french negation analysis . it is a valuable resource for linguistic research and as training data for AI tasks such as negation detection. |