Chinese Named Entity Recognition
37 papers with code • 7 benchmarks • 6 datasets
Chinese named entity recognition is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. from Chinese text (Source: Adapted from Wikipedia).
Benchmarks
These leaderboards are used to track progress in Chinese Named Entity Recognition
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Libraries
Use these libraries to find Chinese Named Entity Recognition models and implementations
OntoNotes 5.0
Weibo NER
Resume NER
Latest papers with no code
A More Efficient Chinese Named Entity Recognition base on BERT and Syntactic Analysis
We propose a new Named entity recognition (NER) method to effectively make use of the results of Part-of-speech (POS) tagging, Chinese word segmentation (CWS) and parsing while avoiding NER error caused by POS tagging error.
Incorporating Uncertain Segmentation Information into Chinese NER for Social Media Text
Chinese word segmentation is necessary to provide word-level information for Chinese named entity recognition (NER) systems.
Porous Lattice-based Transformer Encoder for Chinese NER
Incorporating lattices into character-level Chinese named entity recognition is an effective method to exploit explicit word information.
A Lexicon-Based Graph Neural Network for Chinese NER
Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that sequentially track character and word information have achieved great success.
Classification Attention for Chinese NER
The character-based model, such as BERT, has achieved remarkable success in Chinese named entity recognition (NER).
Adversarial Learning for Chinese NER from Crowd Annotations
To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time.
Using Word Embeddings to Translate Named Entities
In this paper we investigate the usefulness of neural word embeddings in the process of translating Named Entities (NEs) from a resource-rich language to a language low on resources relevant to the task at hand, introducing a novel, yet simple way of obtaining bilingual word vectors.
