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Related Questions
- Can contextualized embeddings improve the accuracy of named entity recognition (NER) by providing more nuanced representations of entities in context?
- How do contextualized embeddings address the issue of word sense disambiguation in NER tasks?
- What are some common applications of contextualized embeddings in NER tasks, and how do they contribute to entity disambiguation?
- Can contextualized embeddings be used to capture entity relationships and hierarchies in text, and if so, how?
- How do contextualized embeddings affect the performance of NER models on out-of-vocabulary words or words with multiple possible meanings?
- Can contextualized embeddings be used to integrate external knowledge sources, such as dictionaries or ontologies, to improve NER accuracy?
- What are some challenges associated with using contextualized embeddings in NER tasks, and how can they be addressed?
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