# Rethinking the Value of Gazetteer in Chinese Named Entity Recognition

arXiv: https://arxiv.org/abs/2207.02802 · HTML: https://mlsanity.com/paper/2207.02802/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [A Fair Comparison of Graph Neural Networks for Graph Classification](https://mlsanity.com/paper/1912.09893/) (2019)
- [Chinese NER Using Lattice LSTM](https://mlsanity.com/paper/1805.02023/) (2018)
- [FLAT: Chinese NER Using Flat-Lattice Transformer](https://mlsanity.com/paper/2004.11795/) (2020)
- [Analogical Reasoning on Chinese Morphological and Semantic Relations](https://mlsanity.com/paper/1805.06504/) (2018)
- [Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning](https://mlsanity.com/paper/1906.01378/) (2019)
- [Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study](https://mlsanity.com/paper/2001.03844/) (2020)
- [Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning](https://mlsanity.com/paper/2004.14074/) (2020)
- [Soft Gazetteers for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2005.01866/) (2020)
- [Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources](https://mlsanity.com/paper/2011.02705/) (2020)
- [Neural Word Segmentation with Rich Pretraining](https://mlsanity.com/paper/1704.08960/) (2017)
- [Self-Attention Gazetteer Embeddings for Named-Entity Recognition](https://mlsanity.com/paper/2004.04060/) (2020)
- [Chinese Named Entity Recognition Augmented with Lexicon Memory](https://mlsanity.com/paper/1912.08282/) (2019)
- [Incorporating Uncertain Segmentation Information into Chinese NER for Social Media Text](https://mlsanity.com/paper/2004.06384/) (2020)

## Built on by (most cited first, 1 of 1)

- [Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering](https://mlsanity.com/paper/2305.08135/) (2023)

## Abstract (arXiv)

Gazetteer is widely used in Chinese named entity recognition (NER) to enhance span boundary detection and type classification. However, to further understand the generalizability and effectiveness of gazetteers, the NLP community still lacks a systematic analysis of the gazetteer-enhanced NER model. In this paper, we first re-examine the effectiveness several common practices of the gazetteer-enhanced NER models and carry out a series of detailed analysis to evaluate the relationship between the model performance and the gazetteer characteristics, which can guide us to build a more suitable gazetteer. The findings of this paper are as follows: (1) the gazetteer improves most of the situations that the traditional NER model datasets are difficult to learn. (2) the performance of model greatly benefits from the high-quality pre-trained lexeme embeddings. (3) a good gazetteer should cover more entities that can be matched in both the training set and testing set.