Run a notebook such as www.tensorflow.org/tutorials/text/word2vec that learns word embeddings from a corpus using the skip-gram approach.

Question:

Run a notebook such as www.tensorflow.org/tutorials/text/word2vec that learns word embeddings from a corpus using the skip-gram approach. Train the model on a corpus of Shakespeare, and separately on a corpus of contemporary language. Then test the resulting word embeddings on a downstream task such as classification of movie reviews. Does one word embedding model outperform the other?

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