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Related Questions
- What are some common sources of ambiguity in entity recognition, such as homographs or context-dependent entities?
- How can machine learning models be trained to handle out-of-vocabulary words or entities in customer feedback data?
- What are some strategies for resolving entity disambiguation, such as using co-reference resolution or entity linking?
- How can entity recognition and disambiguation be improved in the presence of noisy or incomplete data?
- What are some techniques for handling entity evolution or change over time, such as updating entity models or re-training models?
- How can entity recognition and disambiguation be integrated with other natural language processing tasks, such as sentiment analysis or topic modeling?
- What are some common evaluation metrics for entity recognition and disambiguation, and how can they be used to compare different models or approaches?
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