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Knowledge-Based Systems

Quantum Probability-inspired Graph Attention Network for Modeling Complex Text Interaction

Peng Yan, Linjing Li, Daniel Dajun Zeng

Published Oct 2, 2021• 14 citations
Publisher
DOI
10.1016/j.knosys.2021.107557

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In linguistics, the uncertainty of context due to polysemy is widespread, which attracts much attention. Quantum-inspired complex word embedding based on Hilbert space plays an important role in natural language processing (NLP), which fully leverages the similarity between quantum states and word tokens. A word containing multiple meanings could correspond to a single quantum particle which may exist in several possible states, and a sentence could be analogous to the quantum system where particles interfere with each other. Motivated by quantum-inspired complex word embedding, interpretable complex-valued word embedding (ICWE) is proposed to design two end-to-end quantum-inspired deep neural networks (ICWE-QNN and CICWE-QNN representing convolutional complex-valued neural network based on ICWE) for binary text classification. They have the proven feasibility and effectiveness in the application of NLP and can solve the problem of text information loss in CE-Mix [1] model caused by neglecting the important linguistic features of text, since linguistic feature extraction is presented in our model with deep learning algorithms, in which gated recurrent unit (GRU) extracts the sequence information of sentences, attention mechanism makes the model focus on important words in sentences and convolutional layer captures the local features of projected matrix. The model ICWE-QNN can avoid random combination of word tokens and CICWE-QNN fully considers textual features of the projected matrix. Experiments conducted on five benchmarking classification datasets demonstrate our proposed models have higher accuracy than the compared traditional models including CaptionRep BOW, DictRep BOW and Paragram-Phrase, and they also have great performance on F1-score. Eespecially, CICWE-QNN model has higher accuracy than the quantum-inspired model CE-Mix as well for four datasets including SST, SUBJ, CR and MPQA. It is a meaningful and effictive exploration to design quantum-inspired deep neural networks to promote the performance of text classification.
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.
Aspect-Based Sentiment Analysis (ABSA) has gained significant popularity in recent years, which emphasizes the aspect-level sentiment representation of sentences. Current methods for ABSA often use pre-trained models and graph convolution to represent word dependencies. However, they struggle with long-range dependency issues in lengthy texts, resulting in averaging and loss of contextual semantic information. In this paper, we explore how richer semantic relationships can be encoded more efficiently. Inspired by quantum theory, we construct superposition states from text sequences and utilize them with quantum measurements to explicitly capture complex semantic relationships within word sequences. Specifically, we propose an attention-based semantic dependency fusion method for ABSA, which employs a quantum embedding module to create a superposition state of real-valued word sequence features in a complex-valued Hilbert space. This approach yields a word sequence density matrix representation that enhances the handling of long-range dependencies. Furthermore, we introduce a quantum cross-attention mechanism to integrate sequence features with dependency relationships between specific word pairs, aiming to capture the associations between particular aspects and comments more comprehensively. Our experiments on the SemEval-2014 and Twitter datasets demonstrate the effectiveness of the quantum-inspired attention-based semantic dependency fusion model for the ABSA task.