Affiliations: [a]
School of Cybersecurity, Chengdu University of Information Technology
| [b]
College of Science, Chongqing University of Posts and Telecommunications
| [c]
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications
Correspondence:
[*]
Corresponding author. Yan Yu, School of Cybersecurity, Chengdu University of Information Technology. E-mail: yanyu2002034@163.com.
Note: [1] This work was supported by the Scientific Research Fund of Chengdu University of Information Engineering (Grant no. KYTZ2022113)
Note: [2] This work was supported by the National Natural Science Foundation of China (Grant no. 12171065 and 11671001)
Abstract: To mine more semantic information between words, it is important to utilize the different semantic correlations between words. Focusing on the different degrees of modifying relations between words, this article provides a quantum-like text representation based on syntax tree for fuzzy semantic analysis. Firstly, a quantum-like text representation based on density matrix of individual words is generalized to represent the relationship of modification between words. Secondly, a fuzzy semantic membership function is constructed to discuss the different degrees of modifying relationships between words based on syntax tree. Thirdly, the tensor dot product is defined as the sentence semantic similarity by combining the operation rules of the tensor to effectively exploit the semantic information of all elements in the quantum-like sentence representation. Finally, extensive experiments on STS’12, STS’14, STS’15, STS’16 and SICK show that the provided model outperforms the baselines, especially for the data set containing multiple long-sentence pairs, which confirms there are fuzzy semantic associations between words.
Keywords: Quantum-like text representation, fuzzy semantic analysis, fuzzy semantic membership function, neural networks, syntax tree
