Sentence Similarity measurement Based on the combination of Siamese recurrent network model and Word Alignment model
首发时间:2017-12-08
Abstract:In this paper,we introduce a new method to compute the sentences pair similarity which is the combination of a Siamese recurrent neural network model and a word alignment model. In our Siamese recurrent neural network model, we use the original sentence information and interaction information as the input of our network with Google pre-trained word2vec, and calculate the cosine value of the Siamese network outputs as a feature of the sentence pair similarity. While in the alignment model, it calculates the number of alignment word pairs and regards the ration as the sentences pair similarity. In the last we combine the two features in a simple way. In the STS2016 SemEval test data, out model get the state of art result in the news headlines section and the top 2 in the median result.
keywords: Pattern recognition and intelligent system Sentence Similarity Siamese recurrent network Alignment model
点击查看论文中文信息
基于循环神经网络与对齐模型的句子相似度计算
摘要:本文提出一种新的句子相似度计算模型,即通过孪生递归神经网络与对齐模型计算句子相似度。在孪生递归神经网络中我们不仅引入句子本身的信息,还引入了句子与句子之间的交互信息;对齐模型则主要利用对齐词对计算句子相似度。我们的模型在所有参加比赛的队伍中取得总体第二名,新闻标题单项第一的成绩.
基金:
引用
No.****
同行评议
共计0人参与
勘误表
基于循环神经网络与对齐模型的句子相似度计算
评论
全部评论0/1000