关键词: 词向量, 句子相似度, Word2vec, 算法设计

Abstract: Based on the summarization of sentence similarity computing methods, this paper applies 34 000 pieces of texts of People's Daily to train word vector space model for semantic similarity computing. Then, based on the trained word vector model, this paper designs a multi-feature sentence similarity computing method, which takes both word and sentence structure features into consideration. Firstly, the method takes note of possible effects of the number of overlapping words and word continuity, and then applies word vector model to calculate the semantic similarity of non-overlapping words. Regarding the aspect of sentence structure, the method takes both overlapping word order and sentence length conformity into consideration. Finally, this paper designs and implements four different sentence similarity calculating methods, and further develops an experimental system. The experimental results show that the method proposed in this paper can get satisfactory results and the combination and optimization upon the features of words and sentence structures can improve the accuracy of sentence similarity calculating.

Key words: word embedding, sentence similarity, Word2vec, algorithm design