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Related Questions
- How do contextualized embeddings account for nuances in natural language when applied to NER tasks?
- What are the key advantages of using contextualized embeddings with NER compared to traditional word embeddings?
- Can you explain the differences between contextualized embeddings for entity recognition and role-attribute extraction?
- In what ways do contextualized embeddings enable NER to capture more abstract entity types, such as intent or sentiment?
- How can the context obtained from contextualized embeddings help refine the disambiguation of entity types with NER?
- Can you describe how transfer learning with pre-trained contextual embeddings enables NER on specialized domains?
- In which scenarios does contextual information play a critical role for achieving high NER precision on out-of-domain named entity types?
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