mlsanityData to 14 Oct 2026

Named Entity Recognition in Context

arXiv 2503.20836 ↗First posted 26 Mar 2025cs.CLcited by 0

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Abstract (arXiv)

We present the Named Entity Recognition system developed by the Edit Dunhuang team for the EvaHan2025 competition. Our approach integrates three core components: (1) Pindola, a modern transformer-based bidirectional encoder pretrained on a large corpus of Classical Chinese texts; (2) a retrieval module that fetches relevant external context for each target sequence; and (3) a generative reasoning step that summarizes retrieved context in Classical Chinese for more robust entity disambiguation. Using this approach, we achieve an average F1 score of 85.58, improving upon the competition baseline by nearly 5 points.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 14 references that are in the map. First posted on arXiv 26 Mar 2025.

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References · 11

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
  3. 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,989
  4. 2020GLU Variants Improve Transformercited by 1,804
  5. 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,103
  6. 2024Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inferencecited by 554
  7. 2021UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuningcited by 162
  8. 2021Improving Named Entity Recognition by External Context Retrieving and Cooperative Learningcited by 76
  9. 2023Trained on 100 million words and still in shape: BERT meets British National Corpuscited by 39
  10. 2023GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Textscited by 9
  11. 2022Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0cited by 6

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  1. 2024Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative Study
  2. 2023DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition
  3. 2025FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition
  4. 2025NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings
  5. 2023Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset
  6. 2022kNN-NER: Named Entity Recognition with Nearest Neighbor Search
  7. 2024ReverseNER: A Self-Generated Example-Driven Framework for Zero-Shot Named Entity Recognition with Large Language Models
  8. 2026NE-R1: Enhancing Named Entity Recognition Model via Reinforcement Learning