The use of Artificial Intelligence Applications in Corpus-Based Computational Linguistics Research: an integrative analytical approach

Proposing AI-powered linguistic analysis frameworks for specific groups of people including computational linguists and language technology developers has become the current direction for the further advancement of this mode. Grounded in varying interpretations of computational annotation theory and the roles of machine learning algorithms and semantic parsers in delivering scalable corpus analysis, this work constructs a hybrid framework of SEM-validated AI models representing the interaction between linguistic metrics and analytical outcomes. Those models relied upon regression estimates computed from annotated corpus data: the variety of syntactic structures and lexical features that characterize domain-specific language landscapes, and the semantic density, collocation strength, and discourse coherence that shape computational insights. In order to achieve robust analytical validity, this article constructs an integrative analytical framework based on the theory of corpus-driven linguistic modeling, collects data by means of systematic corpus sampling, and uses the method of structural equation modeling to verify the proposed model. Meanwhile, the semantic coherence index and node modularity measures have a moderating effect on the relationship between the annotation accuracy and learner's engagement, but strengthen the indirect path between the semantic clustering and curriculum model's adaptability. SUM modeling effectively accounts for unexpected linguistic deviations in essence (e.g., irregular syntactic patterns) and facilitates quantification of semantic variation, showing implications for better understanding of characteristics of language adaptation mechanisms. Our findings suggest that AI-based computation and corpus analytics can aid in the scalable modeling of language systems, which underlie many educational technologies and natural language applications in multilingual contexts worldwide.

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