Shiru Zhang¹, Zhiyao Liang^{1, *}, Jian Lin²

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 943-957, 2020, DOI:10.32604/cmc.2020.08800

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… More >

Lianggui Liu^1,*, Wei Li¹, Huiling Jia¹

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 97-106, 2018, DOI:10.32604/cmc.2018.03511

Abstract With the rapid development of mobile communication all over the world, the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities. Mobile phone communication data can be regarded as a type of time series and dynamic time warping (DTW) and derivative dynamic time warping (DDTW) are usually used to analyze the similarity of these data. However, many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series. In this paper, a novel hybrid method based on the combination of dynamic time warping… More >