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Related Questions
- How does topic modeling use semantic relationships to identify similar concepts when encountering out-of-vocabulary words?
- Can you explain how topic modeling models use word embeddings to capture semantic relationships and handle out-of-vocabulary words?
- In what ways do topic modeling algorithms like Latent Dirichlet Allocation (LDA) leverage semantic relationships to handle out-of-vocabulary words or concepts?
- How does topic modeling handle polysemous words, which have multiple related meanings, and out-of-vocabulary words?
- Can you discuss the role of semantic relationships in topic modeling when dealing with domain-specific or specialized vocabulary?
- How do topic modeling models use contextual information to infer the meaning of out-of-vocabulary words or concepts?
- What are some common techniques used in topic modeling to handle out-of-vocabulary words or concepts, such as word2vec or GloVe?
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