Morphological analysis is a basic part of natural language processing. So natural language processing system needs a high-performance morphological analyzer to get high-quality results.
Morphological analyzer uses several resources to analyze a sentence, for example, morpheme dictionary, connection rule, phoneme rule and so on. Insufficient quality of morpheme dictionary causes incorrect morphological analysis. For example, if necessary morpheme does not exist in a morpheme dictionary, morphological analyzer cannot make correct analysis. And a morpheme that violates the policy of morphological analyzer causes incorrect morphological analysis. Therefore, it is necessary to improve quality of a morpheme dictionary to improve performance of morphological analysis. For improving quality of a morpheme dictionary, dictionary management work is necessary, but this work is hard and difficult if it is operated manually.
In this thesis, we propose dictionary management workbench to manage a morpheme dictionary more efficiently and more correctly. This workbench offers automatic tools to add nouns to dictionary and validate nouns in dictionary, and offers semi-automatic working environment to manage a morpheme dictionary manually.
By using this workbench, it is possible to improve quality of a morpheme dictionary more efficiently, and experimental results show that morphological analyzer is improved by improving quality of a morpheme dictionary.
