Towards Answering Geography Questions in Gaokao: A Hybrid Approach
Answering geography questions in a university’s entrance exam (e.g., Gaokao in China) is a new AI challenge. In this paper, we analyze its difficulties in problem understanding and solving, which suggest the necessity of developing novel methods. We present a pipeline approach that mixes information retrieval techniques with knowledge engineering and exhibits an interpretable problem solving process. Our implementation integrates question parsing, semantic matching, and spreading activation over a knowledge graph to generate answers. We report its promising performance on a representative sample of 1,863 questions used in real exams. Our analysis of failures reveals a number of open problems to be addressed in the future.
KeywordsInformation retrieval Knowledge engineering Natural language processing Question answering
This work was supported in part by the NSFC under Grants 61772264 and 61572247, in part by the 863 Program under Grant 2015AA015406, and in part by the Qing Lan and Six Talent Peaks Programs of Jiangsu Province.
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