AI-DRIVEN APPROACHES TO CORPUS-BASED LINGUISTIC MODELING
https://doi.org/10.5281/zenodo.20378001
Kalit so‘zlar
artificial intelligence, corpus linguistics, language modeling, machine learning, digital corpora, computational linguistics, neural networks, natural language processing, text mining, big data analysis, semantic analysis, syntactic parsing, linguistic variation, data bias, algorithmic transparency, automated text processing, probabilistic models, transformer models, discourse analysis, sociolinguistics.Annotasiya
The most recent achievements in the development of AI technologies have radically changed the methods and techniques used for corpus-based language modeling that involve the application of large datasets and high computing capacity. By integrating artificial intelligence technologies into corpus linguistics, one can address the issues associated with the vast amount of text material and its inherent complexity in terms of finding underlying connections between different aspects to create advanced models of language behavior. Recent academic studies emphasize the role of machine learning algorithms and neural network technologies in enhancing language modeling and analyzing various lexical, grammatical, and semantic features of digital corpora.
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