In this chapter, we began by extending our discussion on Word2Vec, applied a similar thought process to building document-level embedding, and discussed the Doc2Vec algorithm extensively. We followed that up by building word representations using character n-grams from the words themselves, a technique referred to as fastText. The fastText model helped us capture morphological information from sub-word representations. fastText is also flexible as it can provide embeddings for out-of-vocabulary words since embeddings are a result of sub-word representations. After that, we briefly discussed Sent2Vec, which combines the C-BOW and fastText approaches to building sentence-level representations. Finally, we introduced the Universal Sentence Encoder, which can also be used for fetching sentence-level embeddings and is based on complex deep learning architectures, all of which we will read about in the upcoming chapters.
In the next chapter, we will use whatever we have discussed so...