Research on Text Classification Methods Based on Decision Trees: A Case Study on the Recognition of the Entity Category 'Position'

Qiming Xing; Yankuan Wang

doi:10.70393/616a6e73.323835

OPEN ACCESS |Research Article ||15 April 2025

Research on Text Classification Methods Based on Decision Trees: A Case Study on the Recognition of the Entity Category 'Position'

Qiming Xing

Belarusian State University

xqm200104@gmail.com

Faculty of Applied Mathematics and Computer Science, Belarusian State University, Belarus.

Yankuan Wang

Belarusian State University

wangyankuan21@gmail.com

Faculty of Applied Mathematics and Computer Science, Belarusian State University, Belarus.

* Corresponding Author¹: Qiming Xing, E-Mail: xqm200104@gmail.com

Publication

Accepted 2025 April 10 ; Published 2025 April 15

Academic Journal of Natural Science, 2025, 2(2), 3078-5170.

Abstract

This paper investigates the application of a decision tree model for the binary classification task of the 'Position' category on the CLUENER2020 dataset, aiming to provide a lightweight and efficient method for named entity recognition. The CLUENER2020 dataset includes multiple label categories, among which the accurate identification of the 'Position' category is of significant importance for information extraction and text processing. Through data preprocessing, feature extraction, model training, and testing, this study evaluates the performance of the decision tree model on this task. The experimental results indicate that the model achieves an overall accuracy of 98%, with a precision of 98%, recall of 100%, and F1 score of 99% for the 'Non-Position' category, while the 'Position' category has a precision of 100%, recall of 85%, and F1 score of 92%. Although the model performs excellently on the 'Non-Position' category, the lower recall rate for the 'Position' category reveals a certain degree of missed detection, primarily attributed to the class imbalance in the dataset and the complexity of text features related to positions. The contribution of this paper lies in validating the applicability of traditional machine learning models for specific named entity recognition tasks. Particularly in resource-constrained scenarios, the decision tree model offers a feasible solution. Future research could further enhance model performance and improve the accuracy and robustness of named entity recognition tasks through data augmentation techniques, the integration of more complex model architectures, and in-depth feature engineering and hyperparameter optimization methods.

Keywords

Decision Tree , Named Entity Recognition , CLUENER2020 , Word Classification .

Metadata

DOI:

10.70393/616a6e73.323835

ARK:

ark:/40704/AJNS.v2n2a02

Pages: 10-15

References: 14

Disciplines: Computer Science

Subjects: Data Science

Cite This Article

APA Style

Xing, Q. & Wang, Y. (2025). Research on text classification methods based on decision trees: a case study on the recognition of the entity category 'position'. Academic Journal of Natural Science, 2(2), 10-15. https://doi.org/10.70393/616a6e73.323835

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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