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Sentence Similarity measurement Based on the combination of Siamese recurrent network model and Word Alignment model
ZHAO Yinge 1,XU Weiran 2 *
1.Automation school,Beijing University of Posts and Telecommunications,100876;School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,100876
2.
*Correspondence author
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Funding: none
Opened online: 8 December 2017
Accepted by: none
Citation: ZHAO Yinge,XU Weiran.Sentence Similarity measurement Based on the combination of Siamese recurrent network model and Word Alignment model[OL]. [ 8 December 2017] http://en.paper.edu.cn/en_releasepaper/content/4742223
 
 
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
 
 
 

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