In this lesson, we will try to understand natural language, deep learning & the promise of deep learning for working with text based data.
Natural Language Processing
Natural Language Processing is the idea of transforming free-form text into structured data and back. Often termed as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language.
The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable.
Deep learning can be considered as a subset of machine learning. It is a field that is based on learning and improving on its own by examining computer algorithms. While machine learning uses simpler concepts, deep learning works with artificial neural networks, which are designed to imitate how humans think and learn. Until recently, neural networks were limited by computing power and thus were limited in complexity. However, advancements in big data analytics have permitted larger, sophisticated neural networks, allowing computers to observe, learn, and react to complex situations faster than humans. Deep learning has aided image classification, language translation, speech recognition. It can be used to solve any pattern recognition problem and without human intervention.
Promise of Deep Learning for NLP
Deep learning methods are popular for natural language, primarily because they are delivering on their promise. Some of the first large demonstrations of the power of deep learning were in natural language processing, specifically speech recognition. More recently in machine translation.
The 3 key promises of deep learning for natural language processing are as follows:
Feature Learning. That is, that deep learning methods can learn the features from natural language required by the model, rather than requiring that the features be specified and extracted by an expert.
Continued Improvement. That is, that the performance of deep learning in natural language processing is based on real results and that the improvements appear to be continuing and perhaps speeding up.
End-to-End Models. That is, that large end-to-end deep learning models can be fit on natural language problems offering a more general and better performing approach.
Natural language processing is not solved, but deep learning is required to get you to the state-of-the-art on many challenging problems in the field.