| Project/Area Number |
17500149
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Keio University |
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Principal Investigator |
HAGIWARA Masafumi Keio University, Faculty of Science and Technology, Professor (80198655)
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| Project Period (FY) |
2005 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,890,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥390,000)
Fiscal Year 2007: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2006: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2005: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | Neural network / Language processing / Brain-style / コネクショニストモデル / 推論 / 電子辞書 / EDR |
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| Research Abstract |
This research aims at constructing flexible information processing systems based on brain-style architecture. We have obtained the following significant results :
1) A new vector expression method for word is invented ;
2) Neural network using an electronic dictionary as knowledge base is constructed. Especially, result 1) is very important. So far we did not have suitable expression method of word reflecting distance between words : we have to relate each element in a vector to one word. Therefore in order to make a practical neural network system, we have to treat a vector with hundreds of thousands length. This means neural network style language processing system is almost impossible to construct. Owing to the result of this research, the length of vector is reduced to several hundreds, Which makes possible to create various kinds of sophisticated neural network style language processing systems.
As for the result 2), analogy like inference is realized by a newly proposed system together with the result of 1).
In addition, we created another kind of new neural network. It has a hierarchical structure using an electronic dictionary. This network can do both inductive and deductive inference by learning using an electronic dictionary. The network learns the relation between words from a concept dictionary and knowledge is combined and gives integrated inference results.
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