@Article{cmc.2020.08800,
AUTHOR = {Shiru Zhang, Zhiyao Liang, Jian Lin},
TITLE = {Sentence Similarity Measurement with Convolutional Neural  Networks Using Semantic and Syntactic Features},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {2},
PAGES = {943--957},
URL = {http://www.techscience.com/cmc/v63n2/38553},
ISSN = {1546-2226},
ABSTRACT = {Calculating the semantic similarity of two sentences is an extremely 
challenging problem. We propose a solution based on convolutional neural networks 
(CNN) using semantic and syntactic features of sentences. The similarity score between 
two sentences is computed as follows. First, given a sentence, two matrices are 
constructed accordingly, which are called the syntax model input matrix and the semantic 
model input matrix; one records some syntax features, and the other records some 
semantic features. By experimenting with different arrangements of representing the
syntactic and semantic features of the sentences in the matrices, we adopt the most 
effective way of constructing the matrices. Second, these two matrices are given to two 
neural networks, which are called the sentence model and the semantic model, 
respectively. The convolution process of the neural networks of the two models is carried 
out in multiple perspectives. The outputs of the two models are combined as a vector, 
which is the representation of the sentence. Third, given the representation vectors of two 
sentences, the similarity score of these representations is computed by a layer in the 
CNN. Experiment results show that our algorithm (SSCNN) surpasses the performance 
MPCPP, which noticeably the best recent work of using CNN for sentence similarity 
computation. Comparing with MPCNN, the convolution computation in SSCNN is 
considerably simpler. Based on the results of this work, we suggest that by further 
utilization of semantic and syntactic features, the performance of sentence similarity 
measurements has considerable potentials to be improved in the future.},
DOI = {10.32604/cmc.2020.08800}
}