Deep Learning for Natural Language Processing

doi:10.1002/9781119845041.ch9

Cited by 2 publications

(1 citation statement)

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“…The peculiarities of applying Deep Learning methods for natural language processing (NLP) on the examples of classification and text generation are described in Tuffery S. [58]. In his research, the author focuses on artificial recurrent neural networks (RNNs), as well as transformer models (in particular, BERT).…”

Section: Neural Network Modeling As a Tool For Analyzing Language Unitsmentioning

confidence: 99%

Analysis of the Theoretical Foundations of Neural Network Modeling of Language Unit Recognition

Dovhan¹

2023

Scientific Space: Integration of Traditional and Innovative Processes

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In the context of linguistic science and the integration of the mathematical paradigm into humanitarian discourse, the analysis and processing of natural language (in particular, neural network modeling of language categories) is an important and urgent task. Research by a number of authors and a series of experiments show that artificial neural networks of various types and kinds with different parameterizations can significantly optimize linguistic research: accelerate, deepen, integrate into various scientific fields, etc. At the same time, the use of artificial neural networks in linguistics is an important area of work, as well as a powerful and productive tool for a number of relevant studies, which, however, requires careful analysis and development of implementation strategies. The purpose of the article is to analyze the features of neural network modeling of language units recognition as an effective method of cognition within the anthropocentric paradigm of research. The solution of such research tasks determines the logic of presentation of the studied material in the article: introduction, systematization of achievements in the theory and practice of modeling as a universal tool of scientific cognition in general and theoretical justification of neural network modeling in the context of linguistic paradigm. Methodology of the study is based of the method of analyzing scientific research was updated, which led to the search and analysis of scientific publications related to neural network modeling (in particular, language units). We analyzed more than 60 recent scientific studies and publications covering aspects of the problem under study, which we evaluated based on their relevance, methodological specificity, and scientific novelty. Thus, the research methods outlined in this article allowed us to conduct a thorough analysis of the state and prospects for the development of the theoretical foundations of neural network modeling of language unit recognition. The analysis of the latest scientific research and publications has made it possible to determine the role and place of artificial neural networks of various types and their specifications in the process of modeling language units. The above made it possible to identify the main trends in working with text data aimed at improving the quality of their processing, generation, etc. Results of the survey showed that one of the core tasks of modern linguistic science is to understand the language polysystem, the peculiarities of its structure and the nature of its functioning. In addition, the discursive nature of language practices in the context of data interconnection is also important for documenting language structures and verbal practices that are socially determined. That is why the use of neural networks as a tool for conducting local linguistic and integrated scientific research involving the mathematical paradigm is gaining popularity. Neural network modeling of language categories in this context is the basic basis for such research. Practical implications. The actualization of neural network modeling of linguistic categories is not only one of the means of studying linguistic polysystems, but also an objective criterion of checking the truth of linguistic knowledge. Value/originality. Neural network modeling of language unit recognition has a significant impact on the development, improvement, and evolution of modern linguistic research. The effectiveness of neural network modeling of language categories creates opportunities for a deeper understanding of the studied linguistic objects, phenomena and processes, encouraging linguists to develop various linguistic models that could solve practical linguistic problems (information retrieval, machine translation, natural language processing, knowledge extraction and localization from text, etc.).

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Section: Neural Network Modeling As a Tool For Analyzing Language Unitsmentioning

confidence: 99%