Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Vertical Meaning: Interpersonal Data in Quantum Circuits via Japanese Honorifics

Version 1 : Received: 28 April 2024 / Approved: 29 April 2024 / Online: 29 April 2024 (10:31:52 CEST)

How to cite: Walton, R. D. Vertical Meaning: Interpersonal Data in Quantum Circuits via Japanese Honorifics. Preprints 2024, 2024041892. https://doi.org/10.20944/preprints202404.1892.v1 Walton, R. D. Vertical Meaning: Interpersonal Data in Quantum Circuits via Japanese Honorifics. Preprints 2024, 2024041892. https://doi.org/10.20944/preprints202404.1892.v1

Abstract

This paper proposes a novel concept within Quantum Natural Language Processing (QNLP) to encode the interpersonal metafunction of Systemic Functional Linguistics (SFL), particularly tenor, into quantum circuits by treating Japanese honorifics seriously. Because of English language bias in the literature, the incorporation of nuanced aspects of interpersonal communication evident in languages, such as the aforesaid Japanese, remains under-developed and overlooked. Utilizing lambeq, monoidal categories, and string diagrams, this study extends lambeq’s quantum computing framework to capture not only grammar but also social context—specifically the roles and relationships that define interpersonal interactions in quantum circuits. This approach represents a significant step toward that end simply by defining the honorific type h in lambeq. This innovative strategy exposes the capability of quantum circuits to model the complex structures of language, moving beyond grammar and even semantics—the horizontal, textual dimension of language—to embrace the vertical, hierarchical dimension of human communication and social interaction. Through this lens, the paper underscores the potential of QNLP to transcend traditional linguistic analysis, advocating for a broader and more nuanced understanding of language that includes not only what is said but also the social persona of the interlocutors. An algorithm for parsing Japanese grammar into pregroup diagrams containing the h type is introduced along with a codebase for other researchers to use and to which to contribute. Lastly, a toy experiment is performed to demonstrate that the circuits generated from these pregroup diagrams are suitable for Quantum Machine Learning (QML) applications.

Keywords

Quantum Natural Language Processing; QNLP; Systemic Functional Linguistics; SFL; Japanese; honorifics; interpersonal metafunction; lambeq; category theory; Quantum Machine Learning; QML

Subject

Computer Science and Mathematics, Other

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.