Natural Language Processing with Python

Chapter 9. Building Feature-Based Grammars

Natural languages have an extensive range of grammatical constructions which are hard to handle with the simple methods described in Chapter 8. In order to gain more flexibility, we change our treatment of grammatical categories like S, NP, and V. In place of atomic labels, we decompose them into structures like dictionaries, where features can take on a range of values.

The goal of this chapter is to answer the following questions:

1. How can we extend the framework of context-free grammars with features so as to gain more fine-grained control over grammatical categories and productions?

2. What are the main formal properties of feature structures, and how do we use them computationally?

3. What kinds of linguistic patterns and grammatical constructions can we now capture with feature-based grammars?

Along the way, we will cover more topics in English syntax, including phenomena such as agreement, subcategorization, and unbounded dependency constructions.

Grammatical Features

In Chapter 6, we described how to build classifiers that rely on detecting features of text. Such features may be quite simple, such as extracting the last letter of a word, or more complex, such as a part-of-speech tag that has itself been predicted by the classifier. In this chapter, we will investigate the role of features in building rule-based grammars. In contrast to feature extractors, which record features that have been automatically detected, we are now going to declare ...