There are thousands of papers about natural language processing and computational linguistics, but very few textbooks. I describe the motivation and process for writing a college textbook on natural language processing, and offer advice and encouragement for readers who may be interested in writing a textbook of their own.
The field of Natural Language Processing (NLP) changes rapidly, requiring course offerings to adjust with those changes, and NLP is not just for computer scientists; it's a field that should be accessible to anyone who has a sufficient background. In
this paper, I explain how students with Computer Science and Data Science backgrounds can be well-prepared for an upper-division NLP course at a large state university. The course covers probability and information theory, elementary linguistics, machine and deep learning, with an attempt to balance theoretical ideas and concepts with practical applications. I explain the course objectives, topics and assignments, reflect on adjustments to the course over the last four years, as well as feedback from students.
