Looking forward: Linguistic theory and methods

2025-02-25

AI Summary

This study addresses the growing need to integrate linguistic theory amid the accelerating convergence of computational, cognitive, and evolutionary perspectives. Method: It introduces, for the first time, a systematic integration of these three perspectives: (i) empirically testing symbolic representations using artificial neural networks; (ii) formalizing cognitive mechanisms and evolutionary constraints as testable axioms within formal linguistic theory; and (iii) reconceptualizing intersubjectivity as the foundational property of language. Contribution/Results: The work advances a novel “computation–cognition–evolution” synergistic framework, transcending traditional unidimensional modeling. It elevates neural networks from mere analytical tools to constitutive elements of linguistic theory construction and establishes intersubjectivity as the pivotal bridging concept linking micro-level cognitive processes with macro-level language evolution. The resulting paradigm enhances empirical testability while ensuring cross-disciplinary compatibility, thereby fostering deep integration between formal linguistic theory and empirical sciences.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Humans and AI: Understanding People, Theories, Concepts and Methods

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Computational social science
• Web Mining and Content Analysis: Models for Web evolution

Abstract

This chapter examines current developments in linguistic theory and methods, focusing on the increasing integration of computational, cognitive, and evolutionary perspectives. We highlight four major themes shaping contemporary linguistics: (1) the explicit testing of hypotheses about symbolic representation, such as efficiency, locality, and conceptual semantic grounding; (2) the impact of artificial neural networks on theoretical debates and linguistic analysis; (3) the importance of intersubjectivity in linguistic theory; and (4) the growth of evolutionary linguistics. By connecting linguistics with computer science, psychology, neuroscience, and biology, we provide a forward-looking perspective on the changing landscape of linguistic research.

Problem

• Integration of computational, cognitive, evolutionary perspectives
• Impact of artificial neural networks on linguistics
• Evolution of linguistics with interdisciplinary connections

Innovation

• Integrates computational, cognitive, evolutionary perspectives
• Explicitly tests symbolic representation hypotheses
• Explores neural networks in linguistic analysis