| Grammar Induction |
Article Index for Grammar |
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| CATEGORIES ABOUT GRAMMAR INDUCTION | |
| genetic programming | |
| natural language processing | |
| machine learning | |
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Representations Koza represented Lisp programs as Tree (data Structure) s. He was able to find analogues to the genetic operators within the standard set of tree operators. For example, swapping sub-trees is equivalent to the corresponding process of genetic crossover, where sub-strings of a genetic code are transplanted into an individual of the next generation. Fitness is measured by scoring the output from the Functions of the lisp code. Similar analogues between the tree structured lisp representation and the represenation of grammars as trees, made the application of genetic programming techniques possible for grammar induction. In the case of Grammar Induction, the transplantation of sub-trees corresponds to the swapping of production rules that enable the parsing of phrases from some language. The fitness operator for the grammar is based upon some measure of how well it performed in parsing some group of sentences from the target language. In a tree representation of a grammar, a Terminal Symbol (e.g. a Noun or Verb or some other Part Of Speech ) of a production rule corresponds to a leaf node of the tree. It's parent nodes corresponds to a Non-terminal Symbol (e.g. a Noun Phrase or a Verb Phrase ) in the rule set. Ultimately, the root node might correspond to a sentence non-terminal. Other applications The principle of grammar induction has been applied to other aspects of Natural Language Processing , and have been applied (among many other problems) to Morpheme analysis, and even place name derivations. |
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