"Industrial-Strength Natural Language Processing in Python"
> Spacy is opinionated, in that it typically offers one highly optimized way to do something (whereas nltk offers a huge variety of ways, although they are usually not as optimized). -- Jeremy Howard
Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models | Blog | Explosion AI(About) > A four-step strategy for deep learning with text
> Word embeddings let you treat individual words as related units of meaning, rather than entirely distinct IDs. However, most NLP problems require understanding of longer spans of text, not just individual words. There's now a simple and flexible solution that is achieving excellent performance on a wide range of problems. After embedding the text into a sequence of vectors, bidirectional RNNs are used to encode the vectors into a sentence matrix. The rows of this matrix can be understood as token vectors — they are sensitive to the sentential context of the token. The final piece of the puzzle is called an attention mechanism. This lets you reduce the sentence matrix down to a sentence vector, ready for prediction.