Represent words with embeddings

Check your knowledge

Suppose your text corpus contains 80000 different words. What is typically done to reduce the dimensionality of input vector to neural classifier?

Randomly select 10% of the words and ignore the rest

Use convolutional layer before fully connected classifier layer

Use embedding layer before fully connected classifier layer

Select 10% of most frequently used words and ignore the rest

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