Creating deep neural networks for text classification tasks using grammar genetic programming

Magalhães, Dimmy; Lima, Ricardo Henrique Remes de; Pozo, Aurora

doi:10.1016/j.asoc.2023.110009

Cited by 14 publications

(5 citation statements)

References 16 publications

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“…Compared with the rule-based method, the translation based on statistics is smoother. For a long time, the statistics-based method has been the traditional method of machine interpretation ( Dimmy, Lima & Pozo, 2023 ).…”

Section: Related Workmentioning

confidence: 99%

Fuzzy information recognition and translation processing in English interpretation based on a generalized maximum likelihood ratio algorithm

Yin

2024

PeerJ Computer Science

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English interpretation plays a vital role as a critical link in cross-language communication. However, there are various types of ambiguous information in many interpreting scenarios, such as ambiguity, ambiguous vocabulary, and syntactic structures, which may lead to inaccuracies and fluency issues in translation. This article proposes a method based on the generalized maximum likelihood ratio algorithm (GLR) to identify and process fuzzy information in English interpretation to improve the quality and efficiency of performance. Firstly, we systematically analyzed the common types of fuzzy information in interpretation and delved into the basic principles and applications of the generalized maximum likelihood ratio algorithm. This algorithm is widely used in natural language processing to solve uncertainty problems and has robust modeling and inference capabilities, making it suitable for handling fuzzy information in interpretation. Then, we propose a fuzzy information recognition model based on the generalized maximum likelihood ratio algorithm. This model utilizes a large-scale interpretation corpus for training and identifies potential fuzzy information in the interpretation process through statistical analysis and pattern recognition. Once fuzzy information is detected, we adopt a series of effective translation processing strategies, including contextual inference and adaptation, to ensure the accuracy and naturalness of interpretation. Finally, we conducted a series of experiments to evaluate the performance of the proposed method. The experimental results show that the fuzzy information recognition and translation processing method based on the generalized maximum likelihood ratio algorithm performs well in different interpretation scenarios, significantly improving the quality and fluency of interpretation and reducing ambiguity caused by fuzzy information.

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Section: Related Workmentioning

confidence: 99%

Fuzzy information recognition and translation processing in English interpretation based on a generalized maximum likelihood ratio algorithm

Yin

2024

PeerJ Computer Science

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“…Compared with the rule-based method, the translation based on statistics is smoother. For a long time, the statistics-based method is the traditional method of machine interpretation (Magalhães et al 2023).…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Information Recognition and Translation Processing in English Interpretation based on Artificial Intelligence Recognition Technology

Li¹

2023

Preprint

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Interpretation is an oral expression that converts the information heard and understood in the source language into the target language quickly and accurately, thus completing the role of information transmission. However, language contains too much fuzzy information, so it is inevitable to have fuzzy information in interpretation. The characteristics of fuzzy information, the differences between different languages and cultural backgrounds, and the unpredictability of interpretation have brought great challenges to interpretation. This paper proposes an improved generalized maximum likelihood ratio algorithm (GLR) for fuzzy information processing in English. To improve interpretation accuracy, this study analyzes the characteristics of language databases, vocabulary, grammar, and translation. More specifically, the principle of natural language processing research via intelligent recognition technology is introduced in this study. Secondly, the author introduces the role of vague language in oral communication. Then, this paper introduces the fuzzy language processing method via the improved GLR method in detail. Finally, the experimental results are given to verify the effectiveness of the method.

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“…One of the main tasks of natural language processing (NLP) is the classification of text data, which became the object of Magalhães D., Lima R. H., Pozo A. [37]. This research presents the application of an evolutionary grammar-based approach to the design of deep neural networks (DNNs) using models based on convolutional neural networks of long short-term memory (LSTM) and graph neural networks (GNNs).…”

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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Creating deep neural networks for text classification tasks using grammar genetic programming

Cited by 14 publications

References 16 publications

Fuzzy information recognition and translation processing in English interpretation based on a generalized maximum likelihood ratio algorithm

Fuzzy information recognition and translation processing in English interpretation based on a generalized maximum likelihood ratio algorithm

Fuzzy Information Recognition and Translation Processing in English Interpretation based on Artificial Intelligence Recognition Technology

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

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