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Neurocomputing

QSIM: A Quantum-inspired hierarchical semantic interaction model for text classification

Gao Hui, Peng Zhang, Jing Zhang, Chang Yang

Published Oct 4, 2024• 13 citations
Publisher
DOI
10.1016/j.neucom.2024.128658

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In the field of Natural Language Processing (NLP), especially for many specific tasks, representing and modeling semantic of text plays essential role. Generally speaking, by extracting feature of words, we can find the internal relationship between words and model text semantics. Further more, there have been achievements in using quantum concepts to construct language models, which suggests the effectiveness of applying quantum theory to semantic learning and representation. However, in the current research of quantum-inspired language model, semantic of the text is often directly modeled by quantum concept (e.g., density matrix), which blurs the original meaning of the word itself. In this study, we propose the composite semantic system (CSS) hypothesis and construct a quantum-inspired semantic representation network (QSRN). We apply QSRN to four different text classification tasks, the experimental results show the effectiveness of the algorithm and verify the hypothesis we proposed.
Sentiment classification remains challenging due to subtle semantic cues and long-range contextual dependencies that are often difficult for existing models to capture. Although the Quantum Graph Transformer (QGT) offers a hybrid quantum–classical perspective, its reliance on fully connected graphs and less flexible attention mechanisms limits its effectiveness. To overcome these issues, we introduce the Adaptive Quantum-Inspired Graph Transformer (AQGT), which incorporates an adaptive semantic graph construction method that forms sparse yet informative graphs, along with a quantum-inspired message passing unit for more efficient feature aggregation. AQGT processes text through a multi-layer hybrid architecture that dynamically models semantic structure and enhances node interactions through principled message passing. Experiments on multiple sentiment classification benchmarks show that AQGT achieves consistently stronger performance than classical models and the original QGT. Ablation studies validate the importance of the adaptive graph and message passing components. AQGT further displays good sample efficiency, stable behavior across hyperparameters, and strong qualitative performance in handling nuanced sentiment phenomena such as sarcasm and implicit polarity. These findings demonstrate AQGT’s capability as a robust and expressive model for complex sentiment analysis.