| Research Abstract |
This research deals with the example-based natural language processing using inductive learning . And the research especially focuses to apply genetic algorithms to the example-based machine translation, and to find methods for acquisition of translation rules from small quantity of examples, high speed retrieval of rules and effective deletion of erroneously acquired rules.
To assure the effectiveness of the proposed method, several experiments for the performance evaluation are carried out, and the usefulness of inductive learning to the example-based natural language processing are confirmed.
Principal results of the research are as follows:
1) The usefulness of applying genetic algorithms to the machine translation using inductive learning are confirmed through experiments using the examples from guidebooks of travel english conversation.
2) As the results of above experiments, cases of invalid rule acquisition which are not removed by the selection process of genetic algorithms are found. To resolve this problem, a method using similarities between previous translation examples as constraint conditions to determine the cross point is developed, and the translation quality is improved.
3) This method is applied to translation word selection problem of the machine translation, and the ability of selecting highly appropriate words is proved through experiments.
4) A translation method is proposed , which extracts translation patterns similar to target sentences from examples in a corpus using inductive learning, and the effectiveness of the method is assured by several experiments.
5) As the another types of application of the inductive learning, acquisition of translation rules from surface sentences to semantic expressions, decision of cohesive relations of pronouns, and so on, are studied, and meaningful results are obtained.
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