Semantic Computation In Geography Question Answering

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 based on bag-of-words (BOW), and then extend the framework to Edit Distance variant and BM25 variant. On the other hand, we use convolutional neural network and stacked denoising auto-encoder to generate the distributed representation of sentence semantic respectively. Given the semantic representation of sentence, a logistic regression classifier is employed to classify the sentence. The dataset we use is a large scale Chinese college entrance examination question set of geography, which is clawed from the internet. Experiment results show that the performance of CNN can answer the single-option geography questions with high accuracy, which can achieve 0.7310.