@inproceedings{hammerla-etal-2025-neg,
title = "{D}-Neg: Syntax-Aware Graph Reasoning for Negation Detection",
author = {Hammerla, Leon Lukas and
L{\"u}cking, Andy and
Reinert, Carolin and
Mehler, Alexander},
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-ijcnlp.89/",
pages = "1432--1454",
ISBN = "979-8-89176-303-6",
abstract = "Despite the communicative importance of negation, its detection remains challenging. Previous approaches perform poorly in out-of-domain scenarios, and progress outside of English has been slow due to a lack of resources and robust models. To address this gap, we present D-Neg: a syntax-aware graph reasoning model based on a transformer that incorporates syntactic embeddings by attention-gating. D-Neg uses graph attention to represent syntactic structures, emulating the effectiveness of rule-based dependency approaches for negation detection. We train D-Neg using 7 English resources and their translations into 10 languages, all aligned at the annotation level. We conduct an evaluation of all these datasets in in-domain and out-of-domain settings. Our work represents a significant advance in negation detection, enabling more effective cross-lingual research."
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%0 Conference Proceedings
%T D-Neg: Syntax-Aware Graph Reasoning for Negation Detection
%A Hammerla, Leon Lukas
%A Lücking, Andy
%A Reinert, Carolin
%A Mehler, Alexander
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-303-6
%F hammerla-etal-2025-neg
%X Despite the communicative importance of negation, its detection remains challenging. Previous approaches perform poorly in out-of-domain scenarios, and progress outside of English has been slow due to a lack of resources and robust models. To address this gap, we present D-Neg: a syntax-aware graph reasoning model based on a transformer that incorporates syntactic embeddings by attention-gating. D-Neg uses graph attention to represent syntactic structures, emulating the effectiveness of rule-based dependency approaches for negation detection. We train D-Neg using 7 English resources and their translations into 10 languages, all aligned at the annotation level. We conduct an evaluation of all these datasets in in-domain and out-of-domain settings. Our work represents a significant advance in negation detection, enabling more effective cross-lingual research.
%U https://aclanthology.org/2025.findings-ijcnlp.89/
%P 1432-1454
Markdown (Informal)
[D-Neg: Syntax-Aware Graph Reasoning for Negation Detection](https://aclanthology.org/2025.findings-ijcnlp.89/) (Hammerla et al., Findings 2025)
ACL
- Leon Lukas Hammerla, Andy Lücking, Carolin Reinert, and Alexander Mehler. 2025. D-Neg: Syntax-Aware Graph Reasoning for Negation Detection. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 1432–1454, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.