| Budget Amount *help |
¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2011: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2010: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Research Abstract |
The main goal of our research is to implement the method of using contextual features for improving semantic parsing problems. We also study how unlabeled data could help to implement semantic parsing model further. As a result, we exploited word-cluster models to model a large un-annotated corpus, to extract features for discriminative learning models. In addition, we also introduce a novel semi supervised learning model for semantic parsing with ambiguous supervision. We applied the forest-to-string method for learning the synchronous model between semantic representation and natural language sentence. We also present a novel two-phase framework to learn logical structures of paragraphs in legal articles using machine learning and integer linear programming.
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