Natural Language Processing with Python

For Instructors

Natural Language Processing is often taught within the confines of a single-semester course at the advanced undergraduate level or postgraduate level. Many instructors have found that it is difficult to cover both the theoretical and practical sides of the subject in such a short span of time. Some courses focus on theory to the exclusion of practical exercises, and deprive students of the challenge and excitement of writing programs to automatically process language. Other courses are simply designed to teach programming for linguists, and do not manage to cover any significant NLP content. NLTK was originally developed to address this problem, making it feasible to cover a substantial amount of theory and practice within a single-semester course, even if students have no prior programming experience.

A significant fraction of any NLP syllabus deals with algorithms and data structures. On their own these can be rather dry, but NLTK brings them to life with the help of interactive graphical user interfaces that make it possible to view algorithms step-by-step. Most NLTK components include a demonstration that performs an interesting task without requiring any special input from the user. An effective way to deliver the materials is through interactive presentation of the examples in this book, entering them in a Python session, observing what they do, and modifying them to explore some empirical or theoretical issue.

This book contains hundreds of exercises that can be used as the basis for student assignments. The simplest exercises involve modifying a supplied program fragment in a specified way in order to answer a concrete question. At the other end of the spectrum, NLTK provides a flexible framework for graduate-level research projects, with standard implementations of all the basic data structures and algorithms, interfaces to dozens of widely used datasets (corpora), and a flexible and extensible architecture. Additional support for teaching using NLTK is available on the NLTK website.

We believe this book is unique in providing a comprehensive framework for students to learn about NLP in the context of learning to program. What sets these materials apart is the tight coupling of the chapters and exercises with NLTK, giving students—even those with no prior programming experience—a practical introduction to NLP. After completing these materials, students will be ready to attempt one of the more advanced textbooks, such as Speech and Language Processing, by Jurafsky and Martin (Prentice Hall, 2008).

This book presents programming concepts in an unusual order, beginning with a non-trivial data type—lists of strings—then introducing non-trivial control structures such as comprehensions and conditionals. These idioms permit us to do useful language processing from the start. Once this motivation is in place, we return to a systematic presentation of fundamental concepts such as strings, loops, files, and so forth. In this way, we cover the same ground as more conventional approaches, without expecting readers to be interested in the programming language for its own sake.

Two possible course plans are illustrated in Table 3. The first one presumes an arts/humanities audience, whereas the second one presumes a science/engineering audience. Other course plans could cover the first five chapters, then devote the remaining time to a single area, such as text classification (Chapters 6 and 7), syntax (Chapters 8 and 9), semantics (Chapter 10), or linguistic data management (Chapter 11).

Table 3. Suggested course plans; approximate number of lectures per chapter

Chapter	Arts and Humanities	Science and Engineering
Chapter 1, Language Processing and Python	2–4	2
Chapter 2, Accessing Text Corpora and Lexical Resources	2–4	2
Chapter 3, Processing Raw Text	2–4	2
Chapter 4, Writing Structured Programs	2–4	1–2
Chapter 5, Categorizing and Tagging Words	2–4	2–4
Chapter 6, Learning to Classify Text	0–2	2–4
Chapter 7, Extracting Information from Text	2	2–4
Chapter 8, Analyzing Sentence Structure	2–4	2–4
Chapter 9, Building Feature-Based Grammars	2–4	1–4
Chapter 10, Analyzing the Meaning of Sentences	1–2	1–4
Chapter 11, Managing Linguistic Data	1–2	1–4
Total	18–36	18–36