JACIII Vol.25 No.4 pp. 442-449
doi: 10.20965/jaciii.2021.p0442


Improved Chinese Sentence Semantic Similarity Calculation Method Based on Multi-Feature Fusion

Liqi Liu, Qinglin Wang, and Yuan Li

School of Automation, Beijing Institute of Technology
5 South Zhongguancun Street, Haidian District, Beijing 100081, China

Corresponding author

December 27, 2018
May 10, 2021
July 20, 2021
LSTM, semantic similarity, syntactic component, relative position embedding

In this paper, an improved long short-term memory (LSTM)-based deep neural network structure is proposed for learning variable-length Chinese sentence semantic similarities. Siamese LSTM, a sequence-insensitive deep neural network model, has a limited ability to capture the semantics of natural language because it has difficulty explaining semantic differences based on the differences in syntactic structures or word order in a sentence. Therefore, the proposed model integrates the syntactic component features of the words in the sentence into a word vector representation layer to express the syntactic structure information of the sentence and the interdependence between words. Moreover, a relative position embedding layer is introduced into the model, and the relative position of the words in the sentence is mapped to a high-dimensional space to capture the local position information of the words. With this model, a parallel structure is used to map two sentences into the same high-dimensional space to obtain a fixed-length sentence vector representation. After aggregation, the sentence similarity is computed in the output layer. Experiments with Chinese sentences show that the model can achieve good results in the calculation of the semantic similarity.

Cite this article as:
Liqi Liu, Qinglin Wang, and Yuan Li, “Improved Chinese Sentence Semantic Similarity Calculation Method Based on Multi-Feature Fusion,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.4, pp. 442-449, 2021.
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Last updated on Aug. 03, 2021