Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Journal of the American Society for Information Science
Building a question answering test collection
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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We propose a query expansion method using Genetic Algorithms(GA) in Japanese. Recently, question answering research focuses on contextual questions. Therefore a question answering system has to resolve contextual problems by using both previous questions and previous answers. This problem is largely related to query expansion because of the need to find new keywords. In the contextual processing, a query needs to find other suitable keywords from related resources. Although it is easy for a system to find related words, it is difficult to find a suitable combination of keywords. GA is better suited for a combination problem just like a knapsack problem. Therefore we apply GA to our contextual query expansion method. In the evaluation experiment, MRR was 0.2531 in 360 contextual questions. We confirm the MRR of our method is higher than that of the baseline. We illustrate our method and the experiment.