| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2013: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2012: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2011: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Research Abstract |
In this research, we proposed a method to detect new word senses of a target word from sentences that contain it. To achieve this, we assume a new word sense sentence as an outlier of a data set. Then using outlier detection methods, we detect the new word senses. Generally, outlier detection methods are considered to be unsupervised. However, supervised method is natural for our task. Therefore, our outlier detection method is classified under the supervised framework. We proposed an ensemble method of two methods to detect new word sense sentences: the supervised LOF (Local Outlier Factor) and the supervised generative model. The final output is the intersection of outputs of both methods. We demonstrated the effectiveness of our method using SemEval-2 Japanese WSD task data.
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