# SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER

arXiv: https://arxiv.org/abs/2007.08416 · HTML: https://mlsanity.com/paper/2007.08416/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.06804/) (2021)
- [VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding](https://mlsanity.com/paper/2211.00430/) (2022)
- [Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting](https://mlsanity.com/paper/2204.11406/) (2022)
- [PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection](https://mlsanity.com/paper/2203.04616/) (2022)
- [Type-supervised sequence labeling based on the heterogeneous star graph for named entity recognition](https://mlsanity.com/paper/2210.10240/) (2022)
- [End-to-End Entity Detection with Proposer and Regressor](https://mlsanity.com/paper/2210.10260/) (2022)
- [Research on Named Entity Recognition in Improved transformer with R-Drop structure](https://mlsanity.com/paper/2306.08315/) (2023)

## Abstract (arXiv)

Although character-based models using lexicon have achieved promising results for Chinese named entity recognition (NER) task, some lexical words would introduce erroneous information due to wrongly matched words. Existing researches proposed many strategies to integrate lexicon knowledge. However, they performed with simple first-order lexicon knowledge, which provided insufficient word information and still faced the challenge of matched word boundary conflicts; or explored the lexicon knowledge with graph where higher-order information introducing negative words may disturb the identification. To alleviate the above limitations, we present new insight into second-order lexicon knowledge (SLK) of each character in the sentence to provide more lexical word information including semantic and word boundary features. Based on these, we propose a SLK-based model with a novel strategy to integrate the above lexicon knowledge. The proposed model can exploit more discernible lexical words information with the help of global context. Experimental results on three public datasets demonstrate the validity of SLK. The proposed model achieves more excellent performance than the state-of-the-art comparison methods.