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In this paper, we develop a question answering system for solving single-option geography questions. The system is built in two directions. One computes semantic similarity between two questions. The other converts the task into question sentence binary-classification by generating the distributed representation of sentence semantic. When computing semantic similarity, we first implement a basic framework...
We propose an incremental classifier learning framework that starts with a small amount of labeled training data to create an initial set of classifiers, and gradually incorporates unlabeled data into the incremental learning process to improve the models. A key to the effectiveness of the proposed framework is to judicially select a good incremental learning subset from all remaining unlabeled samples...
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