Foundations of Computational Linguistics: Theory, Methods, and Applications

Dear Colleagues,

Recent advances in large language models (LLMs) and AI agents have enabled powerful applications across psychology, education, healthcare, and social science. However, their rapid deployment has outpaced the development of rigorous mathematical, algorithmic, and formal foundations—raising concerns about reliability, fairness, generalization, and interpretability. To address this challenge, we are pleased to invite submissions to the Special Issue “Foundations of Computational Linguistics: Theory, Methods, and Applications”, which aims to strengthen the mathematical, algorithmic, and formal underpinnings of language technologies while demonstrating their responsible and effective deployment in human-centered domains.

This Special Issue seeks to bridge rigorous computational modeling with real-world impact, emphasizing innovations in representation learning, probabilistic and symbolic reasoning, optimization frameworks, and formal language theory as applied to natural language. In particular, we welcome work that develops or critically evaluates the theoretical guarantees, algorithmic efficiency, statistical robustness, or mathematical structure of modern NLP systems—especially in the era of large language models (LLMs) and autonomous agents.

Topics of interest include, but are not limited to, the following:

• Theoretical and Mathematical Foundations: Formal language theory, information-theoretic analyses, geometric representations of language, convergence and generalization bounds in language models, and complexity of linguistic inference.
• Algorithms and Optimization: Efficient training/inference methods for LLMs, sparse architectures, continual learning, prompt optimization, and mathematically principled alignment techniques.
• Safe and Aligned Language Models: Formal approaches to controllability, verifiability, and robustness; methods for reducing hallucination, bias, and toxicity grounded in statistical or logical frameworks.
• Intelligent Agents with Linguistic Capabilities: Architectures for goal-directed language agents, reinforcement learning with natural language rewards, and formal models of dialogue and cooperation.
• Computational Applications in Human-Centric Domains: Mathematically sound NLP applications in psychology (e.g., computational modeling of affective states from text) and education (e.g., Bayesian knowledge tracing with language feedback, automated scoring via latent semantic metrics).
• Computational Social Science: Quantitative modeling of discourse dynamics, network-based analysis of information diffusion, and causal inference from textual data.

We especially encourage submissions that combine methodological rigor—such as novel loss functions, provable guarantees, or formal evaluation protocols—with meaningful applications, and that clearly articulate the computational or mathematical innovations driving their contributions.

This Special Issue welcomes both theoretical advances and empirically grounded studies that deepen our understanding of how language can be modeled, processed, and deployed through principled computational frameworks.

Dr. Jie Zhou
Guest Editor

Manuscript Submission Information

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