Summary: Introduction to Natural Language Processing

Review what we've learned so far.

Overview of NLP tasks and objectives

In this chapter, we broadly explored NLP to get an impression of the kind of tasks involved in building a good NLP-based system. First, we explained why we need NLP and then discussed various tasks of NLP to generally understand the objective of each task and how difficult it is to succeed at them.

From traditional NLP to deep learning

After that, we looked at the classical approach of solving NLP and went into the details of the workflow using an example of generating sports summaries for soccer games. We saw that the traditional approach usually involves cumbersome and tedious feature engineering. For example, in order to check the correctness of a generated phrase, we might need to generate a parse tree for that phrase. Then, we discussed the paradigm shift that transpired with deep learning and saw how deep learning made the feature engineering step obsolete. We went back to the inception of deep learning and artificial neural networks and worked our way through to the massive modern networks with hundreds of hidden layers. Afterward, we walked through a simple example illustrating a deep model—a multilayer perceptron model—to understand the mathematics taking place in such a model (on the surface of course).

With a foundation in both the traditional and modern ways of approaching NLP, we then discussed the roadmap to understand the topics we will be covering in the course, from learning word embeddings to mighty LSTMs, and to state-of-the-art transformers!

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