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Related Questions
- How does pre-training in topic modeling enable the model to capture nuances and subtleties in language?
- Can you explain the concept of contextualized embeddings and their impact on handling out-of-vocabulary words?
- In what ways does pre-training help to mitigate the problem of unknown or unseen words in topic modeling?
- How does the pre-training process allow topic models to generalize to new, unseen data?
- What is the relationship between pre-training and the quality of topic modeling results in the presence of out-of-vocabulary words?
- Can you discuss the trade-offs between pre-training and fine-tuning in topic modeling for handling out-of-vocabulary words?
- How does the choice of pre-training objective (e.g. masked language modeling, next sentence prediction) affect the model's ability to handle out-of-vocabulary words?
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