关键词: word2vec, 句子相似度, 文本分类, 自动问答, 自然语言处理

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 classification 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.

Key words: word2vec, sentence similarity, text classification, automatic question-answer, natural language processing