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Related Questions
- What are context-aware metrics and how do they differ from traditional metrics in natural language processing?
- Can you provide examples of traditional metrics that struggle to capture subtle variations in language usage?
- In what ways do context-aware metrics, such as named entity recognition and dependency parse trees, improve our ability to analyze language usage?
- How do models like BERT and contextualized embeddings contribute to the development of context-aware metrics?
- What are some challenges associated with developing context-aware metrics, and how can they be addressed?
- Can you describe a scenario where context-aware metrics would be particularly beneficial in analyzing language usage, such as in sentiment analysis or text classification?
- How do context-aware metrics compare to other NLP techniques, such as topic modeling or discourse analysis, in terms of capturing subtle variations in language usage?
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