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Related Questions
- What is the role of pre-training in topic modeling for handling out-of-vocabulary words or concepts?
- How do latent Dirichlet allocation (LDA) and non-negative matrix factorization (NMF) address out-of-vocabulary words in topic modeling?
- Can you explain the concept of word embeddings in topic modeling and how they aid in handling out-of-vocabulary words or concepts?
- In what ways does topic modeling leverage semantic relationships between words to handle out-of-vocabulary words or concepts?
- How does topic modeling handle domain-specific vocabulary and neologisms that may not be present in the training dataset?
- What are some common techniques used in topic modeling to address out-of-vocabulary words or concepts, and how do they compare?
- Can you discuss the impact of hyperparameter tuning on the performance of topic modeling in handling out-of-vocabulary words or concepts?
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