| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Outline of Final Research Achievements |
Developing an appropriate computational mechanism of semantic similarity between linguistic expressions is an important subject for both engineering applications and cognitive science. In this research project, by focusing on evocation relationships of semantic concepts that human beings implicitly organize in their brains, new computational methods for measuring semantic similarity between lexical concepts and for classifying potential semantic relationships between them have been studied. These methods utilize machine learning techniques, including deep neural networks, for integrating linguistic features with image-originated perceptual features, as well as social implications/meanings derived from social image tags. Our methods achieved nealy state-of-the-art results in semantic similarity/relatedness tasks and classification of lexical semantic relations. These results have been discussed in several international and domestic conferences.
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