  The grammars of natural languages may be learned by using genetic algorithms
that reproduce and mutate grammatical rules and part-of-speech tags, improving
the quality of later generations of grammatical components. Syntactic rules are
randomly generated and then evolve; those rules resulting in improved parsing
and occasionally improved retrieval and filtering performance are allowed to
further propagate. The LUST system learns the characteristics of the language
or sublanguage used in document abstracts by learning from the document
rankings obtained from the parsed abstracts. Unlike the application of
traditional linguistic rules to retrieval and filtering applications, LUST
develops grammatical structures and tags without the prior imposition of some
common grammatical assumptions (e.g., part-of-speech assumptions), producing
grammars that are empirically based and are optimized for this particular
application.
