Text is one of the commonly used sequential data types. Text data can be seen as either a sequence of characters or a sequence of words. It is common to see text as a sequence of words for most problems. Deep learning sequential models such as RNN and its variants are able to learn important patterns from text data that can solve problems in areas such as:
- Natural language understanding
- Document classification
- Sentiment classification
These sequential models also act as important building blocks for various systems, such as question and answering (QA) systems.
Though these models are highly useful in building these applications, they do not have an understanding of human language, due to its inherent complexities. These sequential models are able to successfully find useful patterns that are then used for performing different tasks. Applying deep learning...
