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Related Questions
- What are the key differences between contextualized embeddings and traditional word embeddings in NLP tasks?
- How do contextualized embeddings improve the performance of named entity recognition (NER) tasks?
- Can you provide examples of how contextualized embeddings can capture subtle differences in entity types, such as distinguishing between 'Mr.' and 'Mrs.'?
- How do contextualized embeddings handle out-of-vocabulary words and their impact on NER tasks?
- What are some common applications of contextualized embeddings in NLP, and how do they benefit NER tasks?
- Can you explain the concept of 'contextualization' in the context of word embeddings and its role in NER?
- How do contextualized embeddings compare to other techniques for capturing entity type nuances, such as rule-based approaches or machine learning-based methods?
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