Pattern-Matching Rules for the Recognition of Natural Language Dialogue Expressions. AIM-234

Man-machine dialogues using everyday conversational English present
difficult problems for computer processing of natural language.
Grammar-based parsers which perform a word-by-word, parts-of-speech
analysis are too fragile to operate satisfactorily in real time
interviews allowing unrestricted English. In constructing a
simulation of paranoid thought processes, we designed an algorithm
capable of handling the linguistic expressions used by interviewers
in teletyped diagnostic psychiatric interviews. The algorithm uses
pattern-matching rules which attempt to characterize the input
expressions by progressively transforming them into patterns which
match, completely or fuzzily, abstract stored patterns. The power of
this approach lies in its ability to ignore recognized and
unrecognized words and still grasp the meaning of the message. The
methods utilized are general and could serve any "host" system which
takes natural language input.