计算机工程与应用2016，Vol.52Issue(17)：152-159,8.DOI:10.3778/j.issn.1002-8331.1512-0341
面向游戏客服场景的自动问答系统研究与实现
Research and implementation of automatic question-answer system in game customer service scenarios
摘要
Abstract
In view of players’professional and colloquial way of querying in game customer service scenarios, this paper presents a sentence similarity model that takes into account synonymous substitutions, weights, sentence length, word order and other factors with semantic word vectors being established using the deep learning tool word2vec. Based on this model, the drawbacks of both dominance of majority classes and high computational cost associated with KNN classifica-tion algorithm are improved by pre-classification and re-defining classification rules. Furthermore, this paper implements an automatic question-answer system based on text classification for the game customer service scenarios. Experimental results show that this system has higher accuracy and efficiency of queries classification.关键词
word2vec/句子相似度/文本分类/自动问答/自然语言处理Key words
word2vec/sentence similarity/text classification/automatic question-answer/natural language processing分类
信息技术与安全科学引用本文复制引用
王丽月,叶东毅..面向游戏客服场景的自动问答系统研究与实现[J].计算机工程与应用,2016,52(17):152-159,8.基金项目
国家自然科学基金（No.61473089）。 （）
