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Related Questions
- How do Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) algorithms capture semantic relationships between words to enhance out-of-vocabulary word representation?
- Can you explain how LDA and NMF leverage word hierarchies to improve the representation of unknown words in a document?
- How do the algorithms handle polysemy and homographs, and what techniques do they employ to disambiguate word meanings?
- What role does co-occurrence information play in LDA and NMF, and how do they use it to infer relationships between words?
- How do LDA and NMF handle out-of-vocabulary words with no prior knowledge or training data?
- Can you discuss the trade-offs between using LDA and NMF for out-of-vocabulary word representation, and how they compare to other techniques like word embeddings?
- How do LDA and NMF incorporate domain knowledge or expert feedback to improve the accuracy of out-of-vocabulary word representation?
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