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).
Libraries
Use these libraries to find Chinese Named Entity Recognition models and implementationsMost implemented papers
Dice Loss for Data-imbalanced NLP Tasks
Many NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of background examples (or easy-negative examples) overwhelms the training.
Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling
We apply BABERT for feature induction of Chinese sequence labeling tasks.
DiffusionNER: Boundary Diffusion for Named Entity Recognition
In this paper, we propose DiffusionNER, which formulates the named entity recognition task as a boundary-denoising diffusion process and thus generates named entities from noisy spans.
Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning
A bottleneck problem with Chinese named entity recognition (NER) in new domains is the lack of annotated data.
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism
However, existing methods for Chinese NER either do not exploit word boundary information from CWS or cannot filter the specific information of CWS.
CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition
Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters.
Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation
Besides, the training data for CNER in many domains is usually insufficient, and annotating enough training data for CNER is very expensive and time-consuming.
Exploiting Multiple Embeddings for Chinese Named Entity Recognition
Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level.
Dependency-Guided LSTM-CRF for Named Entity Recognition
Dependency tree structures capture long-distance and syntactic relationships between words in a sentence.
Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network
The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system.