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.
2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC FSKD)