Word Sense Disambiguation (WSD) is one of the unresolved issues in natural language processing because polysemous words are inherently ambiguous and require semantic interpretation based on the context. Transformerbased models have made considerable progress in contextual modelling still, they cannot address fine-grained sense distinctions, long-range dependencies, and sentences containing multiple ambiguous words interacting with each other. At the same time quantum-inspired methods also take advantage of superposition and interference to model ambiguity, although generally only at the word level, and do not do contextual propagation. This paper presents Q-EntangleFormer, a new hybrid quantum-classical Transformer based on a unified quantum multi-sense superposition encoding, contextual entanglement of words, and quantum-enhanced attention of deep contextual WSD. The unclear word is represented as a quantum superposition of the possible meanings, the dependence between the context and sense is represented by entanglement networks and the hybrid attention model can combine classical and quantum signals with a fusion gate. Empirical testing of standard WSD metrics indicates that QEntangleFormer has significantly better accuracy than both classical Transformer models and current quantum WSD approaches. The framework also shows that it is parameterefficiently scalable and that it is better understood through amplitude-based semantic reasoning. It therefore connects quantum representation theory and contextual semantic modelling and points to a new direction of quantum-augmented natural language understanding.
Word Sense Disambiguation (WSD) is one of the unresolved issues in natural language processing because polysemous words are inherently ambiguous and require semantic interpretation based on the context. Transformerbased models have made considerable progress in contextual modelling still, they cannot address fine-grained sense distinctions, long-range dependencies, and sentences containing multiple ambiguous words interacting with each other. At the same time quantum-inspired methods also take advantage of superposition and interference to model ambiguity, although generally only at the word level, and do not do contextual propagation. This paper presents Q-EntangleFormer, a new hybrid quantum-classical Transformer based on a unified quantum multi-sense superposition encoding, contextual entanglement of words, and quantum-enhanced attention of deep contextual WSD. The unclear word is represented as a quantum superposition of the possible meanings, the dependence between the context and sense is represented by entanglement networks and the hybrid attention model can combine classical and quantum signals with a fusion gate. Empirical testing of standard WSD metrics indicates that QEntangleFormer has significantly better accuracy than both classical Transformer models and current quantum WSD approaches. The framework also shows that it is parameterefficiently scalable and that it is better understood through amplitude-based semantic reasoning. It therefore connects quantum representation theory and contextual semantic modelling and points to a new direction of quantum-augmented natural language understanding.
The paper proposes a quantum-classical hybrid method of Word Sense Disambiguation (WSD) to combine quantum inspired computation on WSD with classical natural language processing methods. The quantum state encoding is proposed to make ambiguous words coded in the framework as quantum superposition of all the possible senses, and semantic interpretations can be explored by encoding quantum states. A simulation of quantum circuits based on simulated quantum interactions was used to create a hybrid model involving variational quantum circuits and classical neural layer using the PyTorch platform. The system was trained and tested on a manually annotated collection of 5,000 polysemous sentences spanning various domains and tested on WordNet synsets. The experimental findings reveal that the hybrid quantum-classical model of WSD has 94.7 per cent accuracy and weighted F1-score of 94.0 per cent, which is better than CNN-LSTM and Transformer (BERT-base) baselines. The confusion matrix affirms enhanced sense differentiation and less semantic duplicity. The suggested architecture illustrates the possibility of quantum-enhanced natural language processing to be used in order to reach a higher contextual accuracy without compromising the computational efficiency that would be suitable with near-term quantum hardware.
Visual word sense disambiguation focuses on polysemous words, where candidate images can be easily confused. Traditional methods use classical probability to calculate the likelihood of an image matching each gloss of the target word, summing these to form a posterior probability. However, due to the challenge of semantic uncertainty, glosses from different sources inevitably carry semantic biases, which can lead to biased disambiguation results. Inspired by quantum superposition in modeling uncertainty, this paper proposes a Quantum Inference Model for Unsupervised Visual Word Sense Disambiguation (Q-VWSD). It encodes multiple glosses of the target word into a superposition state to mitigate semantic biases. Then, the quantum circuit is executed, and the results are observed. By formalizing our method, we find that Q-VWSD is a quantum generalization of the method based on classical probability. Building on this, we further designed a heuristic version of Q-VWSD that can run more efficiently on classical computing. The experiments demonstrate that our method outperforms state-of-the-art classical methods, particularly by effectively leveraging non-specialized glosses from large language models, which further enhances performance. Our approach showcases the potential of quantum machine learning in practical applications and provides a case for leveraging quantum modeling advantages on classical computers while quantum hardware remains immature.
Quantum-Inspired Transformers are a new class of approaches for natural language processing (NLP) that use principles inspired by physical quantum mechanics to encode and process linguistic information. Paper aims to improve both scalability and efficiency for large-scale NLP tasks in the next generation of artificial intelligence (AI). However, traditional transformers can struggle to encode complex semantic relationships across long sequences and can require high levels of compute resources, hence limiting scalability for large datasets and multilingual tasks. In addition, traditional embeddings do not represent multiple contextual meanings simultaneously, which decreases model expressiveness. To address these challenges and maximize the exploration of potential quantum effects in AI, this paper presents a Quantum State Embedding Attention (QSEA) framework that encodes tokens as quantum inspired states with complex-valued amplitudes. Attention, which is based on entangled states, is meant to efficiently capture long-range dependencies while reducing compute and increasing scalability, all without losing a rich understanding of context. Whereas multilingual translation needs to encode both the source similarity and the target language within a smaller semantic representation space. Our experimentation shows that QSEA consistently provides increased accuracy in decoding, measures attention across long sequences, and yields enduring scalability over large multilingual datasets. The evidence demonstrates the potential for QSEA to be the new layer in the future of AI systems, a layer that is scalable and a highperforming NLP application.
This paper presents a novel algorithm for Word Sense Disambiguation (WSD) based on Quantum Probability Theory.The Quantum WSD algorithm requires concepts representations as vectors in the complex domain and thus we have developed a technique for computing complex word and sentence embeddings based on the Paragraph Vectors algorithm.Despite the proposed method is quite simple and that it does not require long training phases, when it is evaluated on a standardized benchmark for this task it exhibits state-of-the-art (SOTA) performances.
Text classification remains among the most significant tasks in natural language processing and the field has been used in healthcare, law, social media analysis, and economic literature. Classical attention networks existing machine learning approaches and deep learning approaches, including LSTM, BiLSTM, Transformers, and classical attention networks, are susceptible to such problems as long-range dependencies and semantic ambiguity, as well as sparse or underrepresented text. To deal with these deficiencies, this research presents a proposed a Quantum-Enhanced Semantic Attention Network (QESAN), a quantum computing concept integrated into semantic attention-based deep learning paradigm. The proposed QESAN coded textual features using amplitude encoding, quantum superposition, and entanglement-like transformations, such that the model is able to learn not only complex relations between interdependencies, but also fairly subtle semantic relations which are generally hard to learn on classical models. The result of these quantum-enhanced representations is then sent through an attention mechanism which acts on the most semantically significant aspects to produce results that are context-sensitive and can be interpreted. The Kaggle Quantum Programming experimental results indicate that QESAN is effective with accuracy of 98.7 which is far superior than the LSTM, BiLSTM, Transformer, and classical attention networks. The results indicate the ability of QESAN to solve the conventional issues of the text classification process particularly in the assignment of dealing with the long-range dependencies, semantic ambiguity, and sparse data. Overall, the integration of the concept of quantum computing with the attention-based deep learning provides the scalable and resilient solution to the issue of the modern text classification problems of the high-performance.
Abstract Language is not fixed. It is entangled, probabilistic, and observer-dependent. This paper introduces a new paradigm in linguistic theory named "Quantum Linguistics," inspired by the principles of Quantum Physics. Quantum physics and Linguistics have long been regarded as unrelated disciplines,describing nature at the inanimate microlevel on the one hand and language and its scientific study on the other hand. Over the past decades the linguistics has succeeded in providing ever more and refined explanations of generative grammar. Since quantum physics and linguistics deal with the symbolic, rule-governed structure of human language and the non-classical, probabilistic behaviour of subatomic particles, respectively, they have historically been seen as two separate disciplines. This framework allows modeling of polysemy, ambiguity, free word order, and argument structure in a unified, mathematically rigorous manner.To operationalize the model, we implement a Quantum Syntax-Semantics Integration (QSSI) pipeline, which encodes sentences into vector states, applies quantum operations for compositional syntax and semantics, and produces contextually resolved meanings through measurement. Two variants of the pipeline are considered: a classical simulation for experimental validation and a quantum-simulated version for future hardware implementation. The linguistic theory with quantum principles such as superposition, entanglement, and measurement, our model provides a novel probabilistic and context-dependent approach to understand language, explore new insights into human cognitive-linguistic processes, semantic ambiguity observation, and advanced natural language processing applications.