Transfer learning based recurrent neural network algorithm for linguistic analysis

Abstract

AbstractEach language is a system of understanding and skills that allow language users to work together, hypothesize, express thoughts, opinions; wishes need to be articulated. Linguistics is the research of these structures in all respects: the composition, usage, and sociology of language, in particular, are the core of linguistics. Machine learning is the research area that allows machines to learn without being specifically scheduled. In linguistics, the design of writing is understood to be a foundation for many distinct company apps and probably the most useful if incorporated with machine learning methods. Research shows that besides text tagging and algorithm training, there are major problems in the field of big data. This article provides a collaborative effort (transfer learning integrated into recurrent neural network) to analyze the distinct kinds of writing between the language's linear and noncomputational sides and to enhance granularity. In addition to this, this article creates a recurrent neural network model for learning and processing text data automatically. RNN based linguistic process is fast and cost‐effective. It analyses the text data automatically and iteratively. This is the main reason for using RNN for the linguistic process. The outcome demonstrates stronger incorporation of granularity into the language from both sides. Comparative results of machine learning algorithms are used to determine the best way to analyze and interpret the structure of the language.