关键词: 句子相似度, 关系向量模型, 句子语法, 句子语义

Abstract: Sentence similarity computation is very important in all fields of natural language process. Some of the traditional algorithms only compare sentences based on their surface form such as same words, sentence length, word order and do not consider the sentence deep-level semantic information, some methods considered the sentence semantics get an unsatisfactory performance on the algorithm practicality. Therefore, a relation vector model which taking into account the relationship of sentence structure and semantic information based on space vector model is presented, this model is composed of a mix between the key words of the sentence and the key words synonymous information, which reflects local structural component of the sentence as well as the correlation between the local structure and therefore better reflects the structure and semantics of the sentence. An algorithm of sentence similarity based on relation vector model is put forward. The algorithm is applied to the network news summary generation algorithm in order to avoid redundancy. The experimental results show that, compared with the algorithm which considers the word order and semantic, relation vector model algorithm not only improves the accuracy of sentence similarity calculation, the time complexity of calculation is also reduced.

Key words: sentence similarity, relation vector model, sentence syntax, sentence semantics