---
title: "Taking Up the Gaokao Challenge: An Information Retrieval Approach"
authors:
  - "Gong Cheng"
  - "Weixi Zhu"
  - "Ziwei Wang"
  - "Jianghui Chen"
  - "Yuzhong Qu"
doi: ""
url: "https://ijcai.org/proceedings/2016/353"
pdf: "https://ijcai.org/proceedings/2016/0353.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "2479-2484"
track: ""
keywords:
---

# Taking Up the Gaokao Challenge: An Information Retrieval Approach

Gong Cheng, Weixi Zhu, Ziwei Wang, Jianghui Chen, Yuzhong Qu

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 2479-2484.

## Abstract

Answering questions in a university's entrance examination like Gaokao in China challenges AI technology. As a preliminary attempt to take up this challenge, we focus on multiple-choice questions in Gaokao, and propose a three-stage approach that exploits and extends information retrieval techniques. Taking Wikipedia as the source of knowledge, our approach obtains knowledge relevant to a question by retrieving pages from Wikipedia via string matching and context-based disambiguation, and then ranks and filters pages using multiple strategies to draw critical evidence, based on which the truth of each option is assessed via relevance-based entailment. It achieves encouraging results on real-life questions in recent history tests, significantly outperforming baseline approaches.