Abstract
Reading comprehension Question-Answering (QA) for College Entrance Examination (Gaokao in Chinese) is a challenging AI task because it requires effective representation to capture complicated semantic relations between the question and answers. In this paper, a novel method of Chinese Automatic Question-Answering based on a graph is proposed. The method first uses the Chinese FrameNet and discourse topic (paragraph topic sentence and author’s opinion sentence) to construct the affinity matrix between the question and candidate sentences and then employs the algorithm based on the graph to iteratively calculate the importance of each sentence. At last, the top 6 candidate answer sentences are selected based on the ranking scores. The recall on Beijing College Entrance Examination in the recent twelve years is 67.86%, which verifies the effectiveness of the method.
Highlights
Teaching the computer to pass the entrance examination of different education levels, which is an increasingly popular artificial intelligence challenge, has been taken up by researchers in several countries in recent years [1,2,3]. e Todai Robot Project [3] aims to develop a problem-solving system that can pass the University of Tokyo’s entrance examination
(2) Quality assumption: if a page node A is linked by other higher-quality pages, the A page is more important. e reading comprehension QA graph proposed in this paper is derived from the PageRank model. is model makes full use of the correlation between the question and candidate sentences
The top 6 sentences are selected as the final answer sentences. e difference between reading comprehension QA graph and PageRank graph is that, in PageRank network graph, the type of edge connecting nodes is the same, which indicates the recommendation of two website nodes; while the type of edge of reading comprehension graph is different, one is the edge between question and candidate sentence, which represents an association of answer or explanation. e other is the edge between candidate sentence nodes, which represents an association of similar contents between candidate sentences
Summary
Teaching the computer to pass the entrance examination of different education levels, which is an increasingly popular artificial intelligence challenge, has been taken up by researchers in several countries in recent years [1,2,3]. e Todai Robot Project [3] aims to develop a problem-solving system that can pass the University of Tokyo’s entrance examination. China has launched a similar project “key technology and system for language question solving and answer generation,” focusing on studying the human-like QA system for College Entrance Examination (commonly known as Gaokao). Gaokao is a national-wide standard examination for all senior middle school students in China and has been known for its large scale and strictness. The questions are usually given in an implicit way to ask students to dig the exactly expected meaning of the concerned facts. If such kind of meaning fails to fall into the feature representation for either question or answer, the retrieval will hardly be successful
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