How does Turkish Named Entity Recognition work?
- Rule-Based Systems. These systems utilize handcrafted rules based on linguistic properties to identify entities, relying heavily on expert knowledge of the language.
- Statistical Models. Statistical models, such as Hidden Markov Models (HMM) and Conditional Random Fields (CRF), are used to infer the most likely sequence of entity labels based on training data.
- Deep Learning Approaches. Deep learning methods leverage neural networks to automatically learn features from data, achieving high accuracy in named entity classification.
- Pre-trained Language Models. Utilizing models like BERT or Turkish-specific adaptations, these techniques capitalize on transfer learning to enhance NER performance with less labeled data.
