Big Data re-opened the debate between deterministic and probabilistic methods in linguistics. Semantics seems to be a promising field for their application: Greimas observed how semantic layers are stabilized in a document thanks to redundancy, thus transmitting less information. For example, the context stabilizes one of the possible meanings of ambiguous syntactic structures such as “the chicken is ready to eat.” by reducing the probability of the other meanings.Among different probabilistic algorithms, a model based on quantum geometry could better represent natural language semantics. Semantic relationships seem not commutative [3]; they violates Leibniz's law as it happens in quantum logic; many semantic relationships are not represented by morphosyntactic elements, as the vector state is not observable; the stabilisation of a semantic bind could be modelled by entanglement. We will show how the Bell states can be used to encode the basic fundamental semantic relationships represented by Greimas’s semiotic square, thus providing a clue to develop new algorithms designed for information retrieval.
Big Data re-opened the debate between deterministic and probabilistic methods in linguistics. Semantics seems to be a promising field for their application: Greimas observed how semantic layers are stabilized in a document thanks to redundancy, thus transmitting less information. For example, the context stabilizes one of the possible meanings of ambiguous syntactic structures such as “the chicken is ready to eat.” by reducing the probability of the other meanings.Among different probabilistic algorithms, a model based on quantum geometry could better represent natural language semantics. Semantic relationships seem not commutative [3]; they violates Leibniz's law as it happens in quantum logic; many semantic relationships are not represented by morphosyntactic elements, as the vector state is not observable; the stabilisation of a semantic bind could be modelled by entanglement. We will show how the Bell states can be used to encode the basic fundamental semantic relationships represented by Greimas’s semiotic square, thus providing a clue to develop new algorithms designed for information retrieval.
Purpose The paper aims to provide a semiotic interpretation of the role played by entanglement in quantum-based models aimed to information retrieval and suggests possible improvements. Actual models are capable of retrieving documents relevant to a query composed of a keyword and its acceptation expressed by a given context. The paper also considers some analogies between this technique and quantum-based approaches in other disciplines to discuss the consequence of this quantum turn, as epistemology and philosophy of language are concerned. Design/methodology/approach We use quantum geometry to design a formal model for textual semiotics. In particular, the authors refer to Greimas’s work on semantics and information theory, to Eco’s writings on semantic memory and to Lotman’s work on a cybernetic notion of culture. Findings Quantum approaches imply a particular point of view on meaning. Meaning is not a real, positive quality of a given word. It is a net of relations constructed in the text, whose value is progressively determined during the reading process. Furthermore, reading is not a neutral operation: to read is to determine meaning. If it is said that, from a general semiotic point of view, meaning is stored in quantum semantic memories and is read/written by semantic machines, then the operation of “reading/writing” is analogous to the operation of measuring in quantum theory: in other terms, meaning is a value, and this implies an instance (not necessarily human) according to which values are valuable. Research limitations/implications The authors are not proposing a complete quantum semantics. At the present, quantum information retrieval can detect the presence of semantic relations. The authors suggest a way to characterize them, leaving open the problem on how to formalize the document as a vector in four-state semantic space. Practical implications A quantum turn shows deep semiotic implications on the approach to language, which shows an immanent semantic organization not reducible to syntax and morphology. This organization is probabilistic and indeterministic and explains to what extent text fixes the meaning of its lexical units. Social implications In the authors’ perspective, signification is not the exclusivity of a human subject. Criticizing Turing test, the great semiotic and cybernetic scholar Jurij Lotman wrote that if we identify “intelligent” and “human”, we raise the failings of an actual form of intelligence to the rank of an essential characteristic. On this line, meaning is considered as a feature of social, artificial and biological systems. Originality/value The adoption of quantum formalism seems in line with cybernetic framework, involving a probabilistic, non-cartesian point of view on meaning aimed to critically discuss the human–machine relation. Furthermore, Quantum theory (QT) implies a phenomenological point of view on the conditions of possibility of meaning.
A lexeme can be represented as a vector in a Hilbert space. Each element of the vector represents the weight of a semantic relation with a second vector: the context of a second lexeme. Two query operators are defined in a way that they attribute the value +1 to the component of the state that corresponds to the word meaning we are interested in, and -1 in the orthogonal direction. Entangled states have been used to detect semantic relationships in Information Retrieval (IR). In particular, since the first operator corresponds to negation and the conditional gate corresponds to an implication in classical logic, they can represent two basic semantic relationships : hyponymy (e.g. genus-species) and antonymy (e.g. masculine-feminine). In fact, according to Semantics, if A and B are antonymic terms, then A is an hyponym of (i.e. implies) not-B as in the Gremias semiotic square. We underline how the relations of a given semantic universe, and the corresponding result of our operators, depends only on the considered text. Among others, this analogy suggests the application of quantum-based models to language and IR.
The article will explore the philosophical consequence on the notion of meaning of the adoption of a quantum geometry-based approach in linguistic disciplines such Information Retrieval, which importance grew with Internet and big data. The paper reconstructs an archaeology of the turn to probabilistic and a quantum perspectives in language research and its reasons, we considers some linguistic features which justify the adoption of a quantum perspective (non-distributiveness, failures of the substitutivity principle, periodicity of the meaning function), it underlines two features of quantum models (superposition and entanglement) which seems interesting to represent semantics, we hope to show how quantum models allow a convergence of the interest of different perspectives in philosophy of language.
Since 2014 the authors have been developing an algorithm which exports to the field of information retrieval some concepts and models from quantum theory: entanglement and correlation. In this article we explore the linguistic and semiotic implications of a quantum-like semantic theory, reconstructing the relationships between our work and a wide field of study borrowing statistical instruments from quantum theory and applying them to humanities and social sciences. Finally, as an example, we will show how our algorithm works on a case study: religious discourse in new media. In particular, the application of quantum instruments is helpful to understand how the quotation of a Twitter message in the reply modifies its original semantic profile.
This paper explores the potential of leveraging quantum entanglement for efficient computation and storage of semantic information, encompassing concepts and their relationships. The core claim is that utilizing quantum entanglement allows for parallel and distributed processing of semantic data, surpassing the limitations of classical computers in understanding and retrieving semantic information. The proposed mechanism involves encoding semantic information within entangled quantum states and manipulating these states through quantum gates to facilitate parallel computation and rapid exploration of complex relationships. This research represents a novel approach by integrating quantum computing with semantic understanding, offering a pathway to overcome traditional computational bottlenecks in semantic reasoning and information retrieval. The presented framework demonstrates a theoretical foundation for a new paradigm in information processing, focusing on exploiting quantum phenomena for enhanced semantic data manipulation. The key mathematical concepts and operations employed are outlined, providing a foundation for further research and development in this field. ---
While having enhanced NLP, deep learning and pre-trained language models requires a lot of processing power.The work showcases the potential of quantum computing by mapping linguistic data into vast, high-dimensional Hilbert spaces through entanglement and superposition.It focuses on mathematical concepts that set quantum approaches apart from classical ones, among them being the fidelity-based similarity and quantum probability.Various quantum machine learning models are considered in this article, including Quantum Neural Networks and Quantum Support Vector Machines, each discussing the computational advantages in pattern recognition.In addition, it considers retrieval techniques like Grover's algorithm, showing how quantum similarity functions give better semantic search.Indeed, the comparison does show that quantum techniques might yield advantages regarding expressiveness and scalability, despite obstacles such as hardware noise and data encoding.Notwithstanding that quantum technology is still in its infancy, future improvements might advance language understanding.