How does Korean Named Entity Recognition work?
- Rule-based Systems. These systems utilize pre-defined rules and dictionaries to identify and classify named entities based on linguistic patterns.
- Machine Learning Approaches. Machine learning techniques, such as Conditional Random Fields (CRFs), are trained on annotated datasets to predict entity labels in new texts.
- Deep Learning Models. Deep learning models, such as BiLSTM and Transformers, leverage contextual information for improved accuracy in entity recognition.
- Hybrid Models. Hybrid approaches combine rule-based and statistical methods to achieve higher performance by utilizing the strengths of both methods.
