An Introduction to the NLP Landscape

This first chapter is aimed at helping professionals gain a foundation in natural language processing (NLP) by introducing its key concepts, early strategies for machine processing of language, and its synergy with machine learning (ML). We also highlight the importance of mathematical foundations such as linear algebra, statistics, probability, and optimization theory, which are necessary to understand the algorithms used in NLP.

We will discuss some of the initial challenges faced in NLP, such as understanding the context and meaning of words, the relationships between them, and how the traditional methods for understanding those characteristics require labeled data. We will then touch on more recent advancements, including pre-trained language models such as BERT and GPT, and the availability of large amounts of text data, which have led to improved performance on NLP tasks. These models, as we will show later in the book, leverage methods that require much less labeled data.

This introduction will engage you by showing how NLP and ML come together to form more accurate and effective systems, laying the groundwork for the more advanced topics covered later in the book.

We will be covering the following topics in the chapter:

• What is natural language processing?
• Foundational strategies for processing natural language
• The synergy of NLP and ML
• Introduction to math and statistics in NLP