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
Unified Lattice Graph Fusion for Chinese Named Entity Recognition
To solve this issue, we propose a Unified Lattice Graph Fusion (ULGF) approach for Chinese NER.
Improving Chinese Named Entity Recognition by Search Engine Augmentation
In this paper, we propose a neural-based approach to perform semantic augmentation using external knowledge from search engine for Chinese NER.
Application of Data Encryption in Chinese Named Entity Recognition
Recently, with the continuous development of deep learning, the performance of named entity recognition tasks has been dramatically improved.
Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition
Recent years have witnessed the improving performance of Chinese Named Entity Recognition (NER) from proposing new frameworks or incorporating word lexicons.
Using Domain Knowledge for Low Resource Named Entity Recognition
To solve these problems, enlightened by a processing method of Chinese named entity recognition, we propose to use domain knowledge to improve the performance of named entity recognition in areas with low resources.
A New Multifractal-based Deep Learning Model for Text Mining
In this world full of uncertainty, where the fabric of existence weaves patterns of complexity, multifractal emerges as beacons of insight, illuminating them.
DAML: Chinese Named Entity Recognition with a fusion method of data-augmentation and meta-learning
Overfitting is still a common problem in NER with insufficient data.
Distill and Calibrate: Denoising Inconsistent Labeling Instances for Chinese Named Entity Recognition
DCNER consists: (1) a Dual-stream Label Distillation mechanism for distilling five types of inconsistent labeling instances from the noisy data; and (2) a Consistency-aware Label Calibration network for calibrating inconsistent labeling instances based on relatively clean data.
MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity Recognition
Some Chinese characters are quite similar as they share the same components or have similar pronunciations.
Improving Model Generalization: A Chinese Named Entity Recognition Case Study
Specifically, we analyzed five benchmarking datasets for Chinese NER, and observed the following two types of data bias that can compromise model generalization ability.
