Natural Language Processing with Python

First-Order Logic

In the remainder of this chapter, we will represent the meaning of natural language expressions by translating them into first-order logic. Not all of natural language semantics can be expressed in first-order logic. But it is a good choice for computational semantics because it is expressive enough to represent many aspects of semantics, and on the other hand, there are excellent systems available off the shelf for carrying out automated inference in first-order logic.

Our next step will be to describe how formulas of first-order logic are constructed, and then how such formulas can be evaluated in a model.

Syntax

First-order logic keeps all the Boolean operators of propositional logic, but it adds some important new mechanisms. To start with, propositions are analyzed into predicates and arguments, which takes us a step closer to the structure of natural languages. The standard construction rules for first-order logic recognize terms such as individual variables and individual constants, and predicates that take differing numbers of arguments. For example, Angus walks might be formalized as walk(angus) and Angus sees Bertie as see(angus, bertie). We will call walk a unary predicate, and see a binary predicate. The symbols used as predicates do not have intrinsic meaning, although it is hard to remember this. Returning to one of our earlier examples, there is no logical difference between a and b.

Example 10-13. 

1. love(margrietje, brunoke)

2. houden_van(margrietje, brunoke ...