mlsanityData to 14 Oct 2026

Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy

arXiv 2412.08434 ↗First posted 11 Dec 2024cs.CL cs.AIcited by 3

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Abstract (arXiv)

Many previous models of named entity recognition (NER) suffer from the problem of Out-of-Entity (OOE), i.e., the tokens in the entity mentions of the test samples have not appeared in the training samples, which hinders the achievement of satisfactory performance. To improve OOE-NER performance, in this paper, we propose a new framework, namely S+NER, which fully leverages sentence-level information. Our S+NER achieves better OOE-NER performance mainly due to the following two particular designs. 1) It first exploits the pre-trained language model's capability of understanding the target entity's sentence-level context with a template set. 2) Then, it refines the sentence-level representation based on the positive and negative templates, through a contrastive learning strategy and template pooling method, to obtain better NER results. Our extensive experiments on five benchmark datasets have demonstrated that, our S+NER outperforms some state-of-the-art OOE-NER models.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 29 of its 48 references that are in the map. First posted on arXiv 11 Dec 2024.

Builds on

29 of 48 references.

References · 29

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17 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025VCRL: Variance-based Curriculum Reinforcement Learning for Large Language Modelscited by 36
  2. 2025FlowKV: A Disaggregated Inference Framework with Low-Latency KV Cache Transfer and Load-Aware Schedulingcited by 10
  3. 2026Pad=artha: Ontology-Grounded Fine-Grained NER Benchmark for Classical Sanskritcited by 0