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Related Questions
- What are the key advantages of using graph-based models in natural language processing to capture complex contextual relationships?
- How do graph-based models address the issue of polysemy and homograph disambiguation?
- Can you explain the role of edge weights and node representations in graph-based models for context-dependent word meaning?
- What are the challenges of integrating graph-based models with other NLP techniques, such as deep learning and rule-based systems?
- How do graph-based models handle out-of-vocabulary words and words with multiple senses?
- What are some common graph-based architectures used for contextualized word representation, and how do they differ?
- Can you discuss the scalability and computational efficiency of graph-based models for large-scale NLP tasks?
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