Natural Language Processing with PyTorch

Chapter 2. A Quick Tour of Traditional NLP

Natural language processing (NLP, introduced in the previous chapter) and computational linguistics (CL) are two areas of computational study of human language. NLP aims to develop methods for solving practical problems involving language, such as information extraction, automatic speech recognition, machine translation, sentiment analysis, question answering, and summarization. CL, on the other hand, employs computational methods to understand properties of human language. How do we understand language? How do we produce language? How do we learn languages? What relationships do languages have with one another?

In literature, it is common to see a crossover of methods and researchers, from CL to NLP and vice versa. Lessons from CL about language can be used to inform priors in NLP, and statistical and machine learning methods from NLP can be applied to answer questions CL seeks to answer. In fact, some of these questions have ballooned into disciplines of their own, like phonology, morphology, syntax, semantics, and pragmatics.

In this book, we concern ourselves with only NLP, but we borrow ideas routinely from CL as needed. Before we fully vest ourselves into neural network methods for NLP—the focus of the rest of this book—it is worthwhile to review some traditional NLP concepts and methods. That is the goal of this chapter.

If you have some background in NLP, you can skip this chapter, but you might as well stick around for nostalgia ...