A Comprehensive Taxonomy of Negation for NLP and Neural Retrievers

• URL: http://arxiv.org/abs/2507.22337v1
• Date: Wed, 30 Jul 2025 02:44:20 GMT
• Title: A Comprehensive Taxonomy of Negation for NLP and Neural Retrievers
• Authors: Roxana Petcu, Samarth Bhargav, Maarten de Rijke, Evangelos Kanoulas,
• Abstract summary: We introduce a taxonomy of negation that derives from philosophical, linguistic, and logical definitions.<n>We generate two benchmark datasets that can be used to evaluate the performance of neural information retrieval models.<n>We propose a logic-based classification mechanism that can be used to analyze the performance of retrieval models on existing datasets.
• Score: 61.086220009192424
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding and solving complex reasoning tasks is vital for addressing the information needs of a user. Although dense neural models learn contextualised embeddings, they still underperform on queries containing negation. To understand this phenomenon, we study negation in both traditional neural information retrieval and LLM-based models. We (1) introduce a taxonomy of negation that derives from philosophical, linguistic, and logical definitions; (2) generate two benchmark datasets that can be used to evaluate the performance of neural information retrieval models and to fine-tune models for a more robust performance on negation; and (3) propose a logic-based classification mechanism that can be used to analyze the performance of retrieval models on existing datasets. Our taxonomy produces a balanced data distribution over negation types, providing a better training setup that leads to faster convergence on the NevIR dataset. Moreover, we propose a classification schema that reveals the coverage of negation types in existing datasets, offering insights into the factors that might affect the generalization of fine-tuned models on negation.

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