| 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. |
Similar Papers
NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution (2020.lrec-1)
Copied to clipboard
| 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. |
Improving negation detection with negation-focused pre-training (2022.naacl-main)
Copied to clipboard
| 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. |
Learning with Structured Representations for Negation Scope Extraction (P18-2)
Copied to clipboard
| 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. |
Predicting the Focus of Negation: Model and Error Analysis (2020.acl-main)
Copied to clipboard
| 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. |
Improving Feature Extraction for Pathology Reports with Precise Negation Scope Detection (C18-1)
Copied to clipboard
| 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. |
Negation Scope Conversion: Towards a Unified Negation-Annotated Dataset (2024.lrec-main)
Copied to clipboard
| 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. |
Understanding by Understanding Not: Modeling Negation in Language Models (2021.naacl-main)
Copied to clipboard
| Challenge: | Negation is a core construction in natural language, but state-of-the-art pre-trained language models often handle it incorrectly. |
| Approach: | They propose to augment language modeling objective with unlikelihood objective based on negated generic sentences from a raw text corpus. |
| Outcome: | The proposed approach reduces the top 1 error rate to 4% on negated LAMA dataset and improves on negating NLI benchmarks. |
ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning (2023.acl-short)
Copied to clipboard
| Challenge: | Negation is a ubiquitous but complex linguistic phenomenon that poses a significant challenge for NLP systems. |
| Approach: | They propose a benchmark that measures how well models handle natural language negation . they extend ScoNe-NLI to embed negation reasoning in short narratives . |
| Outcome: | The proposed model can reason about negation, but struggles to do so on NLI examples outside of its core pretraining regime. |
An Analysis of Natural Language Inference Benchmarks through the Lens of Negation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing benchmarks for natural language inference ignore negations and can make inferences that are difficult to make. |
| Approach: | They propose a new benchmark for natural language inference in which negation plays a critical role. |
| Outcome: | The proposed benchmarks show that negation plays a critical role in inference judgments. |
CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Negation is fundamental to human communication. |
| Approach: | They propose a dataset which requires reasoning about implications of negated statements in paragraphs . they collect paragraphs with diverse negation cues and crowdworkers ask questions about implications . |
| Outcome: | The first dataset in english requires reasoning about implications of negated statements in paragraphs . it features 14,182 question-answer pairs with over 200 unique negation cues based on crowd-workers . the best performing model achieves only 42% on consistency metric, well below human performance of 81%. |