Simplify the Usage of Lexicon in Chinese NER

By Minlong Peng, Ruotian Ma, Qi Zhang, Xuanjing Huang. Annual Meeting of the Association for Computational Linguistics, 2019

304 citations recorded by the source corpus.

Recently, many works have tried to augment the performance of Chinese named entity recognition (NER) using word lexicons. As a representative, Lattice-LSTM has achieved new benchmark results on several public Chinese NER datasets. However, Lattice-LSTM has a complex model architecture. This limits its application in many industrial areas where real-time NER responses are needed. In this work, we propose a simple but effective method for incorporating the word lexicon into the character representations. This method avoids designing a complicated sequence modeling architecture, and for any neural NER model, it requires only subtle adjustment of the character representation layer to introduce the lexicon information. Experimental studies on four benchmark Chinese NER datasets show that our method achieves an inference speed up to 6.15 times faster than those of state-of-the-art methods, along with a better performance. The experimental results also show that the proposed method can be easily incorporated with pre-trained models like BERT.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/17eb7e129d1155d9e5b42f54e7c17d05a6ce57c8

How researchers see this paper

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In Artificial Intelligence

1. Chong Teng
2. Donghong Ji
3. Shengqiong Wu
4. Jingye Li
5. Fei Li
6. Jiang Liu
7. Meishan Zhang
8. Hao Fei

Citations in the corpus

• Unified Named Entity Recognition as Word-Word Relation Classification (2021, methodology): “Tagging-based methods , which assign a tag to every token with different label schemes, such as BIO (Lample et al. 2016), BIOHD (Tang et al. 2018), and BIEOS (Li et al. 2020b; Ma et al. 2020).”