Sentences can be thought of as combinations of words, such that words are spoken over time in a sequential manner. It is essential to capture this temporal relationship in natural language data. The presence of a word in a lot of scenarios might be influenced by words not necessarily in the immediate neighborhood. Think of the following sentences:
She went on a walk along with her dog.
He went on a walk with his dog.
The sentences are exactly similar except in the usage of words for the identification of gender. The usage of the term her or his is directly dependent on the term She or He used toward the beginning of the sentence. With CNNs, we only looked at the immediate proximity of a word. Text data, as we saw in the examples, offers a unique challenge wherein we need to preserve context and have some notion of memory, which can help in making judgments at various points in time. RNNs are the go-to thing in such scenarios as they keep a notion of...
