Rtx voice performance impact11/10/2023 Natural language processing is one of the most important components of text classification. It is widely used in numerous fields, such as internet information filtering, question and answer topic classification, intelligent recommendation systems, sentiment analysis, and public opinion analysis. Text classification is the process of determining the text category according to natural language text under the predefined category set, which means assigning predefined category tags to the text. įunding: The author(s) received no specific funding for this work.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The data that support the findings of this study are available from. Received: Accepted: SeptemPublished: October 12, 2023Ĭopyright: © 2023 Zhang et al. Universiti Teknikal Malaysia Melaka Fakulti Teknologi Maklumat dan Komunikasi, MALAYSIA We find that the best macro-f1s for categorizing text for the two datasets are 92.13% and 91.99%, which represent improvements of 0.3% and 2%, respectively over the compared baselines.Ĭitation: Zhang D, Li J, Xie Y, Wulamu A (2023) Research on performance variations of classifiers with the influence of pre-processing methods for Chinese short text classification. Our general conclusion is that the systematic use of preprocessing methods can have a positive impact on the classification of Chinese short text, using classification evaluation such as macro-F1, combination of preprocessing methods such as word segmentation, Chinese specific stop word and symbol removal, and classifier selection such as machine and deep learning models. Finally, we conducted a battery of various additional experiments, and found that most of the classifiers improved in performance after proper preprocessing was applied. We then explored the influence of the preprocessing methods on the final classifications according to various conditions such as classification evaluation, combination style, and classifier selection. In this paper we experimentally compared fifteen commonly used classifiers on two Chinese datasets using three widely used Chinese preprocessing methods that include word segmentation, Chinese specific stop word removal, and Chinese specific symbol removal. At present, however, most of the studies on this topic focus on exploring the influence of preprocessing methods on a few text classification algorithms using English text. Text pre-processing is an important component of a Chinese text classification.
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