Abstract
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.
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Dulewicz, G., Unold, O. (2002). Evolving Natural Language Parser with Genetic Programming. In: Abraham, A., Köppen, M. (eds) Hybrid Information Systems. Advances in Soft Computing, vol 14. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1782-9_27
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DOI: https://doi.org/10.1007/978-3-7908-1782-9_27
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