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. |
| Approach: | They propose a machine learning system that processes negation in Spanish . they use a corpus from the SFU corpus to perform two tasks . |
| Outcome: | The proposed system outperforms state-of-the-art in negation cue detection and scope identification. |
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A review of Spanish corpora annotated with negation (C18-1)
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| Challenge: | Existing corpora annotated with negation information are small and not always compatible . negation is a linguistic phenomenon that is not addressed in English . |
| Approach: | They review existing corpora annotated with negation in Spanish and analyze compatibility . they propose to develop a supervised negation processing system for Spanish . |
| Outcome: | The proposed system will not be able to merge the small corpora in Spanish due to lack of compatibility in annotations. |
Learning with Structured Representations for Negation Scope Extraction (P18-2)
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| Challenge: | Existing approaches to negation scope detection have been criticized for capturing information related to negations, long-distance dependencies and structural information. |
| Approach: | They propose to use conditional random fields, semi-Markov CRF and latent-variable CRF models to capture useful information such as long-distance dependencies and some latent structural information. |
| Outcome: | The proposed approaches can capture useful information such as features related to negation cue, long-distance dependencies and some latent structural information. |
NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution (2020.lrec-1)
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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. |
| Outcome: | The proposed model outperforms existing systems on the BioScope Corpus, the Sherlock dataset and the SFU Review Corpus in scope resolution. |
Negation Scope Conversion: Towards a Unified Negation-Annotated Dataset (2024.lrec-main)
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| Challenge: | Negation scope resolution models that use pre-trained language models perform worse when fine-tuned on a combined dataset. |
| Approach: | They propose to automatically convert the negation scopes of BioScope and SFU to those of Sherlock and merge them into a unified dataset. |
| Outcome: | The proposed method improves on the unified dataset compared to the simply combined dataset. |
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. |
An Analysis of Negation in Natural Language Understanding Corpora (2022.acl-short)
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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. |
| Outcome: | The proposed corpora have few negations compared to general-purpose English and are often unimportant . state-of-the-art transformers obtain significantly worse results with instances that contain negation, especially if the negations are important. |
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). |
| Approach: | They propose a new learning strategy for negation building on ELECTRA’s replaced token detection objective. |
| Outcome: | The proposed approach leads to substantial gains on a variant of RTE with additional negation. |
Predicting the Focus of Negation: Model and Error Analysis (2020.acl-main)
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| Challenge: | Experimental results show that a scope detector can predict the focus of negation . negation is a complex phenomenon present in all human languages . |
| Approach: | They propose to leverage a scope detector to introduce the scope of negation as an additional input to the neural network. |
| Outcome: | The proposed model obtains the best results to date, and analyzes errors depending on scope and context information. |
This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have grammatical knowledge but fail to interpret negation . a recent study shows that LLMs struggle with negative sentences . |
| Approach: | They propose to use a dataset to grasp LLMs' generalization and inference capability . they also fine-tuned models to assess whether the understanding of negation can be trained . |
| Outcome: | The proposed model is able to generalize and infer negation in 400,000 sentences . but it is suboptimal when it comes to negation, a key step in natural language processing . |
Towards the Roots of the Negation Problem: A Multilingual NLI Dataset and Model Scaling Analysis (2025.findings-emnlp)
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| Challenge: | Negations are key to determining sentence meaning, making them essential for logical reasoning. |
| Approach: | They construct and publish two new textual entailment datasets in four languages with paired examples differing in negation. |
| Outcome: | The results show that increasing the model size may improve the models’ ability to handle negations. |