Chinese Question Classification Based on ERNIE and Feature Fusion

Gao-jun, Liu; Yuan, Qiuxia; Duan, Jianyong; Kou, Jie; Wang, Hao

doi:10.1007/978-3-030-60457-8_28

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Recently, medical Q&A and text classification methods have been greatly improved, however, recent studies rarely considered this issue from the perspective of feature fusion. It was noteworthy that Liu et al [26] proposed a new method for Chinese question classification from the perspective of ERNIE and feature fusion. They firstly developed a generalized language representation model by integrating knowledge, then adopted the technology of Highway-CNN and Highway-DCU-BiLSTM to extract local features and sequence features separately, and finally integrated the two features by a linear formula.…”

Section: B Related Methodsmentioning

confidence: 99%

An LSTM&Topic-CNN Model for Classification of Online Chinese Medical Questions

2021

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In recent years, people's interest in health question and answer (Q&A) websites has been growing with the development of the internet technologies. How to seek appropriate professional medical information among the massive data has become the focus of all patients. Therefore, it is vital to obtain reasonable predictions and automatic recommendations on the basis of patients' keyword descriptions of their health status and question intention. The key to solving this problem is to achieve automatic text classification of health questions. This paper considered a feature fusion model for the classification of Chinese short texts on medical health Q&A websites by combining the text features and topic features. Firstly, we generated the text word vector by word embedding method and obtained the text features under Long Short-Term Memory (LSTM) model. Given the difficulty in determination of topic numbers, we conducted a sub-sample experiment to obtain the few optimal topic numbers under which the classification performances were good. Then we extracted the topic features and used the one-dimensional convolution idea of the Convolutional Neural Network (CNN) model for topic feature filtering. Finally, we combined the two features together subtly for text classification. Two experiments were conducted to illustrate our model in terms of recall rate, precision, and F1 value when the datasets were from different online medical Q&A websites. Results showed that the LSTM&Topic-CNN model could efficiently enhance the classification effect of Chinese medical health question texts.INDEX TERMS Chinese medical Q&A text, long short-term memory, latent dirichlet allocation, convolutional neural network, feature fusion.

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Section: B Related Methodsmentioning

confidence: 99%

An LSTM&Topic-CNN Model for Classification of Online Chinese Medical Questions

2021

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“…ERNIE is a pretrained language model for Chinese corpus. Question classification has been resolved by considering the ERNIE pretraining model together with feature fusion [42]. Specifically, RoBERTa [43] has been implemented and fine-tuned for Chinese text classification, and ChineseBERT [44] incorporates both Chinese character glyph and pinyin information.…”

Section: Plos Onementioning

confidence: 99%

Research on performance variations of classifiers with the influence of pre-processing methods for Chinese short text classification

Zhang,

Li,

Xie

et al. 2023

PLoS ONE

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Text pre-processing is an important component of a Chinese text classification. 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. 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. 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. Finally, we conducted a battery of various additional experiments, and found that most of the classifiers improved in performance after proper preprocessing was applied. 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. 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.

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