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Related Questions
- How do contextualized embeddings like BERT, RoBERTa, and Longformer improve the representation of entity meanings in a sentence?
- Can you elaborate on how contextualized embeddings learn to capture the nuances of entity meaning, such as implicit and explicit relationships?
- How do contextualized embeddings differ from traditional word embeddings in terms of their ability to capture entity meaning?
- What are some common applications of contextualized embeddings in natural language processing tasks that involve entity meaning, such as question answering and text classification?
- How do contextualized embeddings handle out-of-vocabulary words and entities, and what impact does this have on entity meaning representation?
- Can you provide examples of how contextualized embeddings can be fine-tuned for specific entity types, such as person, organization, or location?
- What are the limitations of contextualized embeddings in capturing entity meaning, and how can these limitations be addressed through future research?
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