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Related Questions
- How do techniques like smoothing and interpolation help mitigate the impact of out-of-vocabulary words in topic modeling?
- Can you explain the differences in how Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) handle rare terms?
- How do algorithms like Latent Semantic Analysis (LSA) and Latent Semantic Indexing (LSI) handle out-of-vocabulary words and rare terms?
- What are some common strategies for handling out-of-vocabulary words and rare terms in topic modeling, such as using subword models or word embeddings?
- How do topic modeling algorithms like LDA and NMF perform when dealing with very large vocabularies or sparse data?
- Can you discuss the trade-offs between using techniques like dimensionality reduction and handling out-of-vocabulary words and rare terms in topic modeling?
- How do algorithms like Topic Modeling using Regularized Bootstrapping (TM-RB) and Online LDA handle out-of-vocabulary words and rare terms?
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