| Budget Amount *help |
¥3,830,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥630,000)
Fiscal Year 2009: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2008: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2007: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Research Abstract |
In order to realize a multilingual question answering system, several monolingual question systems will be required as its components. However, rule based approach is too expensive to prepare several monolingual question systems. Therefore, machine learning approach is employed to prepare them in this study, instead of rule based approach. We showed that machine learning approach is effective to prepare question answering systems in cheaper costs than rule based approaches with competitive performance. In order to improve their performances, two researches were conducted: large scale bilingual dictionary to improve keyword translation performance, and analysis of functional relationship between keywords to improve answer selection performance.
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