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    <abstract>Named entity recognition (NER) extracts entity spans and assigns them to predefined types. Large language models (LLMs) are increasingly used for NER, but most LLM-based methods still rely on a mainly semantic view, judging whether a candidate span is compatible with an entity type. Although effective, this view can miss entities with weak type semantics, over-accept semantically salient modifiers, and leave little chance to recover spans omitted by early candidate extraction. We argue that entity spans are also traceable in sentence form: local structure and prepositions provide cues about a span’s position and function. We propose SSDA-NER, a Semantic-Structural Dual-path Arbitration framework that combines semantic understanding with structural analysis. Specifically, the Semantic path checks candidate type compatibility, while the Structure path withholds explicit semantic guidance during its first stage and identifies possible mentions through structural analysis. The Arbitration Agent then compares their rationales and confidence scores to adjudicate disagreements. Experiments on nine datasets with three backbone LLMs show that the two views are complementary: the Structure path substantially supplements entities missed by the Semantic path on some datasets, and arbitration outperforms existing multi-agent methods in most evaluated settings. Source code is available at https://github.com/MrHoux/SSDA-NER.</abstract>
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%0 Conference Proceedings
%T Structure Matters Semantic-Structural Dual-Path Arbitration for Named Entity Recognition with LLMs
%A Hou, Xinghong
%A Tong, Yiqi
%A Chen, Wei
%A Guo, Wei
%A Zhuang, Fuzhen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F hou-etal-2026-structure
%X Named entity recognition (NER) extracts entity spans and assigns them to predefined types. Large language models (LLMs) are increasingly used for NER, but most LLM-based methods still rely on a mainly semantic view, judging whether a candidate span is compatible with an entity type. Although effective, this view can miss entities with weak type semantics, over-accept semantically salient modifiers, and leave little chance to recover spans omitted by early candidate extraction. We argue that entity spans are also traceable in sentence form: local structure and prepositions provide cues about a span’s position and function. We propose SSDA-NER, a Semantic-Structural Dual-path Arbitration framework that combines semantic understanding with structural analysis. Specifically, the Semantic path checks candidate type compatibility, while the Structure path withholds explicit semantic guidance during its first stage and identifies possible mentions through structural analysis. The Arbitration Agent then compares their rationales and confidence scores to adjudicate disagreements. Experiments on nine datasets with three backbone LLMs show that the two views are complementary: the Structure path substantially supplements entities missed by the Semantic path on some datasets, and arbitration outperforms existing multi-agent methods in most evaluated settings. Source code is available at https://github.com/MrHoux/SSDA-NER.
%U https://aclanthology.org/2026.findings-emnlp.1267/
%P 26795-26812

Markdown (Informal)

[Structure Matters Semantic-Structural Dual-Path Arbitration for Named Entity Recognition with LLMs](https://aclanthology.org/2026.findings-emnlp.1267/) (Hou et al., Findings 2026)

• Structure Matters Semantic-Structural Dual-Path Arbitration for Named Entity Recognition with LLMs (Hou et al., Findings 2026)

ACL

• Xinghong Hou, Yiqi Tong, Wei Chen, Wei Guo, and Fuzhen Zhuang. 2026. Structure Matters Semantic-Structural Dual-Path Arbitration for Named Entity Recognition with LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 26795–26812, Budapest, Hungary. Association for Computational Linguistics.