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Related Questions
- How does word2vec and GloVe handle rare words, and what are the implications for their performance on large datasets?
- What are the key differences between word2vec and GloVe in terms of vectorization, and how do these differences impact their effectiveness on rare words?
- Can you explain how the skip-gram and CBOW models in word2vec compare to GloVe's matrix factorization approach, and how they affect the representation of rare words?
- In a dataset with a high proportion of rare words, what are the trade-offs between using word2vec or GloVe, and how can these trade-offs be optimized?
- How do the dimensionality of the word embeddings and the training data size impact the performance of word2vec and GloVe on rare words?
- Can you describe the effects of subsampling frequent words on the representation of rare words in both word2vec and GloVe?
- How do the pre-trained word embeddings from word2vec and GloVe compare to those trained from scratch on a large dataset, especially for rare words?
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