Prediction of Chinese Semantic Word-Building Patterns Based on Complex Network Features

Minfeng Wang,Xin Ning

doi:10.1155/2022/4162998

Abstract

The research on prediction of Chinese semantic word-formation patterns based on complex network features has certain practical and theoretical significance in the field of natural language understanding. In this paper, complex networks are introduced into the prediction of Chinese semantic word-formation patterns, and a new prediction method of Chinese semantic word-formation patterns based on complex networks is proposed. And a solution that combines the semantic word-building rules of Chinese language with pattern recognition algorithm is put forward. Aiming at this scheme, a variety of pattern recognition algorithms are compared and analyzed, and the most suitable binary logistic regression model and naive Bayes model are found to predict Chinese semantic word-building patterns. The semantic loss is reduced, and the text classification model and corresponding classification algorithm are constructed, by introducing the maximum common subgraph theory to calculate text similarity under the complex network representation. The results of the experiments show that using complex networks to predict Chinese semantic word-formation patterns is both effective and feasible. The computer can judge the semantic word-formation pattern more accurately using the semantic word-formation pattern prediction model based on this theory.

Highlights

With the explosive growth of Internet information, automatic text classification technology, which can facilitate users to locate the required information quickly and accurately, becomes more and more important [1]
In the text representation method based on vector space model widely used in traditional text classification, it is easy to cause the lack of text semantic information [2] because it assumes that the feature words are independent of each other, that is, it ignores the semantic relationship between words
The semantic information contained in Chinese text is richer than that contained in other languages that focus on form and structure, making it more difficult for a text representation model based on vector space to fully describe and predict the semantic information contained in Chinese text [11]. erefore, this paper proposes a prediction model of Chinese semantic word formation based on complex network features

Summary

Introduction

With the explosive growth of Internet information, automatic text classification technology, which can facilitate users to locate the required information quickly and accurately, becomes more and more important [1]. E focus of solving compound word formation is to straighten out the complex semantic relationship between word meaning and morpheme meaning. In order to solve the lack of text structure and semantic information in the above vector space model, in this paper, the complex network theory is introduced into the prediction of Chinese semantic word-formation mode, and the semantic theory is effectively integrated with text representation. The semantic information contained in Chinese text is richer than that contained in other languages that focus on form and structure, making it more difficult for a text representation model based on vector space to fully describe and predict the semantic information contained in Chinese text [11]. Erefore, this paper proposes a prediction model of Chinese semantic word formation based on complex network features. Simulation results show that this method can significantly improve the efficiency and accuracy of unlisted word interpretation, ambiguity elimination, dictionary compilation, and machine translation

Related Work

Methodology

Result

TFIDF Method of this article

Conclusions