Linguistics Encyclopedia.pdf

The linguistics encyclopedia 40
The other programs of this period did little more syntactic processing, but did at least
do some work on the patterns that they extracted. A reasonable example is Bobrow’s
(1968) program for solving algebra problems like the following:
If the number of customers Tom gets is twice the square of 20 per cent of
the number of advertisements he runs, and the number of advertisements
he runs is 45, what is the number of customers Tom gets?
This appears to be in English, albeit rather stilted English. Bobrow’s program processed
it by doing simple pattern matching to get it into a form which was suitable for his
equation-solving program. It is hard to say whether what Bobrow was doing was really
language processing, or whether his achievement was more in the field of equation
solving. It is clear that his program would have made no progress whatsoever with the
following problem:
If the number of customers Tom gets is twice the square of 20 per cent of
the number of advertisements he runs, and he runs 45 advertisements, how
many customers does he get?
The other pattern-matching programs of this time were equally frail in the face of the real
complexity of natural language. It seems fair to say that the main progress made by these
programs was in inference, not in language processing. The main lesson for language
processing was that pattern matching was not enough: what was needed was proper
linguistic theory.
LINGUISTIC THEORY
The apparent failure of the early work made AI researchers realize that they needed a
more adequate theory of language. As is far too often the case with AI work, there was
already a substantial body of research on the required properties of language which had
been ignored in the initial enthusiasm for writing programs. Towards the end of the
1960s, people actually went away and read the existing linguistic literature to find out
what was known and what was believed, and what they might learn for their next
generation of programs. Simultaneously, it was realized that NLP systems would need to
draw on substantial amounts of general knowledge about the world in order to determine
the meanings in context of words, phrases, and even entire discourses. Work in the late
1960s and early 1970s concentrated on finding computationally tractable versions of
existing theories of grammar, and on developing schemes of meaning representation.
These latter are required both to enable the integration of the specifically linguistic part of
an NLP system with the sort of knowledge required for disambiguation and interpretation
in context, and to actually link the NLP system to some other program which had
information a user might want to access.
SYNTACTIC THEORY