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| 1 | Pediatric reference intervals in China(PRINCE):design and rationale for a large,multicenter collaborative cross-sectional study显示文摘There is a lack of accurate pediatric reference intervals(RIs) in China, with most commonly used RIs established without consideration of the effect of age and gender. The Pediatric Reference Intervals in China(PRINCE) project aims to establish and verify pediatric RIs for 31 common laboratory measurands.The project will be a large, multicenter cross-sectional study:14,490 healthy children and adolescents aged up to 19 years will be surveyed by 10 children's hospitals and one pediatric department of a university hospital. To evaluate the feasibility and efficiency of the study methods, 602 children were surveyed in the pilot phase of the PRINCE study in April 2017: it found that some measurands were distinctly age dependent and that there were differences between values for males and females. The results of the pilot study affirmed the necessity of the PRINCE project for Chinese pediatrics. The pilot also indicated potential difficulties in the full survey, e.g., difficulties in recruiting children aged under 3 years and insufficient collection of blood samples from infants. The operation of the PRINCE project has been modified based on the findings in the pilot study toward improving the validity of the PRINCE project and promoting its openness and transparency. | Xin Ni Wenqi Song Xiaoxia Peng Ying Shen Yaguang Peng Qiliang Li Yan Wang Lixin Hu Yanying Cai Hong Shang Min Zhao Hong Jiang Yaoguo Huang Runqing Mu Wenxiang Chen Mingting Peng Chuanbao Zhang Jie Zeng Chenbin Li Hongling Yang Yongmei Jiang Jin Xu Guixia Li Hongbing Chen Yun Xiang Sancheng Cao Zhenxin Guo Dapeng Chen | 2018 | Science Bulletin2018，63，24： | 12 |
| 2 | p-Norm Broad Learning for Negative Emotion Classification in Social Networks显示文摘Negative emotion classification refers to the automatic classification of negative emotion of texts in social networks.Most existing methods are based on deep learning models,facing challenges such as complex structures and too many hyperparameters.To meet these challenges,in this paper,we propose a method for negative emotion classification utilizing a Robustly Optimized BERT Pretraining Approach(RoBERTa)and p-norm Broad Learning(p-BL).Specifically,there are mainly three contributions in this paper.Firstly,we fine-tune the RoBERTa to adapt it to the task of negative emotion classification.Then,we employ the fine-tuned RoBERTa to extract features of original texts and generate sentence vectors.Secondly,we adopt p-BL to construct a classifier and then predict negative emotions of texts using the classifier.Compared with deep learning models,p-BL has advantages such as a simple structure that is only 3-layer and fewer parameters to be trained.Moreover,it can suppress the adverse effects of more outliers and noise in data by flexibly changing the value of p.Thirdly,we conduct extensive experiments on the public datasets,and the experimental results show that our proposed method outperforms the baseline methods on the tested datasets. | Guanghao Chen Sancheng Peng Rong Zeng Zhongwang Hu Lihong Cao Yongmei Zhou Zhouhao Ouyang Xiangyu Nie | 2022 | Big Data Mining and Analytics2022，5，3： | 0 |
| 3 | Deep Broad Learning for Emotion Classification in Textual Conversations显示文摘Emotion classification in textual conversations focuses on classifying the emotion of each utterance from textual conversations.It is becoming one of the most important tasks for natural language processing in recent years.However,it is a challenging task for machines to conduct emotion classification in textual conversations because emotions rely heavily on textual context.To address the challenge,we propose a method to classify emotion in textual conversations,by integrating the advantages of deep learning and broad learning,namely DBL.It aims to provide a more effective solution to capture local contextual information(i.e.,utterance-level)in an utterance,as well as global contextual information(i.e.,speaker-level)in a conversation,based on Convolutional Neural Network(CNN),Bidirectional Long Short-Term Memory(Bi-LSTM),and broad learning.Extensive experiments have been conducted on three public textual conversation datasets,which show that the context in both utterance-level and speaker-level is consistently beneficial to the performance of emotion classification.In addition,the results show that our proposed method outperforms the baseline methods on most of the testing datasets in weighted-average F1. | Sancheng Peng Rong Zeng Hongzhan Liu Lihong Cao Guojun Wang Jianguo Xie | 2024 | Tsinghua Science and Technology2024，29，2： | 0 |
| 4 | A survey on deep learning for textual emotion analysis in social networks显示文摘Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,and natural language processing.This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research.In this paper,we provide an overview of TEA based on DL methods.After introducing a background for emotion analysis that includes defining emotion,emotion classification methods,and application domains of emotion analysis,we summarize DL technology,and the word/sentence representation learning method.We then categorize existing TEA methods based on text structures and linguistic types:text-oriented monolingual methods,text conversations-oriented monolingual methods,text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods.We close by discussing emotion analysis challenges and future research trends.We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development. | Sancheng Peng Lihong Cao Yongmei Zhou Zhouhao Ouyang Aimin Yang Xinguang Li Weijia Ji Shui Yu | 2022 | Digital Communications and Networks2022，8，5： | 0 |
| 5 | CNN-Based Broad Learning for Cross-Domain Emotion Classification显示文摘Cross-domain emotion classification aims to leverage useful information in a source domain to help predict emotion polarity in a target domain in a unsupervised or semi-supervised manner.Due to the domain discrepancy,an emotion classifier trained on source domain may not work well on target domain.Many researchers have focused on traditional cross-domain sentiment classification,which is coarse-grained emotion classification.However,the problem of emotion classification for cross-domain is rarely involved.In this paper,we propose a method,called convolutional neural network(CNN)based broad learning,for cross-domain emotion classification by combining the strength of CNN and broad learning.We first utilized CNN to extract domain-invariant and domain-specific features simultaneously,so as to train two more efficient classifiers by employing broad learning.Then,to take advantage of these two classifiers,we designed a co-training model to boost together for them.Finally,we conducted comparative experiments on four datasets for verifying the effectiveness of our proposed method.The experimental results show that the proposed method can improve the performance of emotion classification more effectively than those baseline methods. | Rong Zeng Hongzhan Liu Sancheng Peng Lihong Cao Aimin Yang Chengqing Zong Guodong Zhou | 2023 | Tsinghua Science and Technology2023，28，2： | 0 |
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