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Related Questions
- How do graph-based models handle the issue of scalability in large-scale NLP tasks such as text classification and sentiment analysis?
- What are some common techniques used to improve the computational efficiency of graph-based models for NLP tasks?
- Can you explain the trade-off between model accuracy and computational efficiency in graph-based models for NLP?
- How do graph-based models handle out-of-vocabulary words and unseen entities in large-scale NLP tasks?
- What is the impact of graph-based models on the computational resources required for training and inference in NLP tasks?
- Can graph-based models be used for multi-task learning in NLP, and if so, how?
- How do graph-based models handle the issue of overfitting in large-scale NLP tasks, and what techniques can be used to prevent it?
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