Inproceedings,
Evolving Natural Language Parser with Genetic Programming
2001 International Workshop on Hybrid Intelligent Systems, page 361--378. Adelaide, Australia, Springer-Verlag, (11-12 December 2001)
Abstract
1 Introduction When we try to deal with natural
language processing (NLP) we have to start with a
grammar of a natural language. But the grammars
described in linguistic literature have an informal
form and many exceptions. Thus, they are not useful to
create final formal models of grammars, which make
machine processing of sentences possible. These
grammars can be a starting point for the attempts to
create basic models of natural language grammar at the
most. However, it requires expert knowledge. Machine
learning based on a set of sample sentences can be the
better way to find the grammar rules. This kind of
learning allows to avoid the preparation of knowledge
about the language for the NLP system. The examples of
correct and incorrect sentences allow the NLP systems
with the self-evolutionary parser to try to find the
right grammar. This self-evolutionary parser can be
improved on basis of new examples. Thus, the knowledge
acquired in this way is flexible and easyly
modifiable.
