Open Access
ARTICLE
Sentence Similarity Measurement with Convolutional Neural Networks Using Semantic and Syntactic Features
Shiru Zhang1, Zhiyao Liang1, *, Jian Lin2
1 Faculty of Information Technology, Macau University of Science and Technology, Macau.
2 Management Information Systems, University of Houston-Clear Lake, Houston, 77058, USA.
* Corresponding Author: Zhiyao Liang. Email: .
Computers, Materials & Continua 2020, 63(2), 943-957. https://doi.org/10.32604/cmc.2020.08800
Received 10 October 2019; Accepted 05 November 2019; Issue published 01 May 2020
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.
Keywords
Cite This Article
S. Zhang, Z. Liang and J. Lin, "Sentence similarity measurement with convolutional neural networks using semantic and syntactic features,"
Computers, Materials & Continua, vol. 63, no.2, pp. 943–957, 2020. https://doi.org/10.32604/cmc.2020.08800