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Hands-On Natural Language Processing with Python by Rajalingappaa Shanmugamani, Rajesh Arumugam

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Word embeddings

As discussed in the chapter on word embeddings, we would like to build a dense vector that is capable of capturing the semantic meaning of the context in which the word is being used. In this task, however, we will build our word embeddings as a concatenation of pretrained embeddings extracted at the word level and trained embeddings from the character level. Hence, the word embedding, , is composed of a pretrained word-level vector, , and a trained character-level vector, .

Although it is possible to encode the character-level ...

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