关键词: 问题求解, 词向量, 相似度计算, 问题发现

Abstract: Artificial Intelligence（AI） is more popular today, intelligence problem-solving, therefore, has gradually become a major research method. This paper mainly studies the multiple choice problem-solving on the basis of knowledge association and reasoning, tries to solve two problems of the question-comprehension and similar question discovery. According to question-comprehension, TextRank and Part-Of-Speech（POS） tagging are introduced in order to complete the key information extract, and word2vec word clustering result is used to complete the key information extension. As for similar problem-discovery, firstly, this paper has randomly select questions from question bank management system according to the key information which is generated by problem-comprehension. Then, sentence similarity is calculated by using BM25 variant, lexical item vector weighted and improved editing distance respectively, combining with word vector which generated by Word2vec. According to the index of similarity to confirm the answer, questions solved. In the related experiment of the geographical multiple choice, an average accuracy rate of 75.88% is obtained, the feasibility of the problem-solving is verified.

Key words: problem-solving, word2vec, similarity computing, problem discovery