Development research of highly accurate Question Answering system doing precise matching of meaning

我们已经开发了Metis，这是一个问答系统，它通过将问题图与知识图匹配来找到答案。问题图是作为问题句的语义分析的结果而获得的，使用从问题句中提取的关键字从数据库中检索的知识句中类似地分析知识图。在检索这样的知识句子时，系统使用搜索引擎Lucene，基于从问题图中提取的搜索关键字来搜索和收集它们。为了提取答案，METIS计算问题和知识图之间的相似度，进行精确匹配。在该匹配中，系统计算相似度，该相似度是与问题图中的节点相对应的知识图中的节点的所有组合相对于问题图的相似性同现图的相对大小。然后，该系统选择具有最高相似度的知识图，并从中提取对应于给定疑问词的部分。系统会将这一部分作为答案。评估实验使用了100个百万富翁问题。在前三个答案中获得正确答案的准确率为74%。此外，它还参加了NTCIR的评估竞赛，提出事实问题的QAC的准确率为18%，询问原因、方法等的CLQA的准确率为22%。

We have developed Metis, a question-answering system that finds an answer by matching a question graph with the knowledge graphs. The question graph is obtained as a result of semantic analysis of a question sentence, the knowledge graphs are similarly analyzed from knowledge sentences retrieved from a database using keywords extracted from the question sentence. In retrieving such knowledge sentences, the system searches for and collects them using Lucene, a search engine, based on search keywords extracted from the question graph. To extract the answer, Metis calculates the degrees of similarity between the question and knowledge graphs to conduct precise matching. In this matching, the system calculates the degrees of similarity, which is the relative size of the similarity co-occurrence graph to the question graphs with respect to all combinations of nodes in the knowledge graph corresponding to those in the question graph. The system then chooses the knowledge graph with the highest degree of similarity and extracts from it the portion that corresponds to the given interrogative word. The system presents this portion as the answer. The evaluation experiment was done by using 100 quiz millionaire questions. The precision to obtain the correct answer within the first three answers was 74%. Moreover, it participated in the evaluation contest in NTCIR, and the precision was 18% in QAC which asked factoid question, the precision was 22% in CLQA which asked the reasons, the method and etc.