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Deep Broad Learning for Emotion Classification in Textual Conversations

Tsinghua Science and Technology 2024年第2期29卷 481-491页

作者： Sancheng Peng Rong Zeng Hongzhan Liu Lihong Cao Guojun Wang Jianguo Xie Center for Linguistics and Applied Linguistics and Laboratory of Language Engineering and ComputingGuangdong University of Foreign StudiesGuangzhou 510006China Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices South China Normal UniversityGuangzhou 510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou 510006China Modern Education Technology Center Guangdong University of Foreign StudiesGuangzhou 510006China

Emotion classification in textual conversations focuses on classifying the emotion of each utterance from textual *** is becoming one of the most important tasks for natural language processing in recent ***,it is a challenging task for machines to conduct emotion classification in textual conversations because emotions rely heavily on textual *** address the challenge,we propose a method to classify emotion in textual conversations,by integrating the advantages of deep learning and broad learning,namely *** 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 *** 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 *** addition,the results show that our proposed method outperforms the baseline methods on most of the testing datasets in weighted-average F1.

关键词： emotion classification textual conversation Convolutional Neural Network(CNN) Bidirectional Long Short-Term Memory(Bi-LSTM) broad learning

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Readability-Guided Idiom-Aware Sentence Simplification (RISS) for Chinese 23rd

Readability-Guided Idiom-Aware Sentence Simplification (RISS...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Jingshen Chen, Xinglu Qiu, Xinying Wang, Zhimin Feng, Wenhe School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Faculty of Chinese Language and Culture Guangdong University of Foreign Studies Guangzhou China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9789819783663

Chinese sentence simplification faces challenges due to the lack of large-scale labeled parallel corpora and the prevalence of idioms. To address these challenges, we propose Readability-guided Idiom-aware Sentence Simplification (RISS), a novel framework that combines data augmentation techniques. RISS introduces two key components: (1) Readability-guided Paraphrase Selection (RPS), a method for mining high-quality sentence pairs, and (2) Idiom-aware Simplification (IAS), a model that enhances the comprehension and simplification of idiomatic expressions. By integrating RPS and IAS using multi-stage and multi-task learning strategies, RISS outperforms previous state-of-the-art methods on two Chinese sentence simplification datasets. Furthermore, RISS achieves additional improvements when fine-tuned on a small labeled dataset. Our approach demonstrates the potential for more effective and accessible Chinese text simplification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Multi-task learning

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Intelligent Clinical Diagnosis and Treatment Model for Hemorrhagic Stroke using Ensemble Learning Algorithms 5

Intelligent Clinical Diagnosis and Treatment Model for Hemor...

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5th International Conference on Artificial Intelligence and Computer engineering, ICAICE 2024

作者： Liang, Xingcheng Zhang, Zhenhua Mo, RuJian Zhou, ZhiBiao Guangdong University of Foreign Studies Laboratory of Language Engineering and Computing Guangzhou China School of Mathematics and Statistics Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9798331528911

Hemorrhagic stroke accounts for 10-15% of stroke cases, posing a significant burden on patients and society. This study aims to develop an intelligent clinical diagnosis and treatment model for hemorrhagic stroke by integrating statistical models and algorithms-Bayesian parameter-optimized logistic regression, random forest, XGBoost, LightGBM, and CatBoost-with patient imaging data to improve treatment outcomes and prognosis accuracy. We utilized follow-up imaging data within 48 hours of hemorrhagic stroke onset to explore factors related to hematoma expansion risk. Feature engineering was performed using a random forest classifier for feature selection, followed by constructing logistic regression and random forest models optimized through Bayesian parameter search to predict hematoma expansion probabilities, evaluated with the Brier score. Additionally, we developed XGBoost, LightGBM, and CatBoost models to predict patients' 90-day modified Rankin Scale (mRS) scores, analyzing the relationship between prognosis and factors such as personal history, disease history, treatment methods, and imaging features, with model performance compared using mean squared error (MSE). The results indicated that the random forest model was superior in predicting hematoma expansion risk, while the CatBoost model outperformed both XGBoost and LightGBM for 90-day mRS score prediction. Prognostic analysis revealed that hematoma volume, edema volume, and age positively influenced mRS scores, whereas a history of stroke did not significantly impact mRS scores. Continuous monitoring of hematoma and edema volumes, along with age considerations, is crucial for effective patient management. © 2024 IEEE.

关键词： Logistic regression

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A survey on textual emotion cause extraction in social networks

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Digital Communications and Networks 2025年第2期11卷 524-536页

作者： Sancheng Peng Lihong Cao Guojun Wang Zhouhao Ouyang Yongmei Zhou Shui Yu Laboratory of Language Engineering and Computing Center for Linguistics and Applied LinguisticsGuangdong University of Foreign StudiesGuangzhou510006China School of English Education Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China South China University of Technology Guangzhou510006China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science University of Technology SydneySydneyNSW 2007Australia

With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their *** and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing *** Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language *** is different from the previous tasks of emotion recognition and emotion *** addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion *** this paper,we provide a survey for ***,we introduce the development process and classification of ***,we discuss the existing methods and key factors for ***,we enumerate the challenges and developing trend for TECE.

关键词： Text Emotion Emotion cause Machine learning Deep learning

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A Method for Automatic English Oral Fluency Scoring 17

A Method for Automatic English Oral Fluency Scoring

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Li, Su-Mei Zhu, Zi-Xi Li, Xiao-Ning Li, Xin-Guang Guangdong University of Foreign Studies Laboratory of Language Engineering and Computing Guangdong Guangzhou China Experimental Teaching Center Guangdong University of Foreign Studies Guangdong Guangzhou China School of Information Science and Technology Guangdong University of Foreign Studies Guangdong Guangzhou China Guangdong University of Foreign Studies Guangdong Guangzhou China

ISBN: (纸本)9798331507398

In order to address the issue of incomplete feature extraction in traditional oral fluency scoring methods, a method based on feature fusion is proposed in the paper, which integrates traditional acoustic features and deep features of audio to complete the scoring of oral fluency. For the audio to be tested, IS10_paraling features are extracted as traditional acoustic features, and a fine-tuned wav2vec2.0 pre-trained model is used to extract deep features of the audio. These two types of features are concatenated to form fusion features, which are then input into a multilayer perceptron scoring model to obtain the oral fluency score. Experimental results on the speechocean762 dataset show that compared to scoring methods based on single types of audio features, the fusion feature-based method improves scoring performance. The multilayer perceptron scoring model based on fusion features achieves a Pearson correlation coefficient of 0.744 with human scoring, demonstrating the effectiveness of the proposed method. © 2024 IEEE.

关键词： Regression analysis

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Multimodal language Data and Platform Construction for LCTLs Teaching 23

Multimodal Language Data and Platform Construction for LCTLs...

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15th International Conference on Machine Learning and computing, ICMLC 2023

作者： Wang, Yuan Jin, Kai Liu, Wuying Shandong Key Laboratory of Language Resources Development and Application Ludong University China School of Foreign Languages Qilu University of Technology China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies China

ISBN: (纸本)9781450398411

The progress of artificial intelligence technology has brought great opportunities for the development of Less Commonly Taught languages (LCTLs) teaching. However, the scarcity of LCTLs resources still plagues the current teaching platform. This paper is committed to building multimodal language data including foreign language vocabulary and translation, bilingual example sentences, phonetic symbols and audio, morphological similarity recommendation, semantic similarity recommendation, etc., and using full-text index and Web Service technology to build a new LCTLs teaching platform. The detailed steps are: (1) acquiring and processing multimodal language data through web pages such as TED and Opensubtitles;(2) constructing the inverted indexes used the HashMap and storing the index files in the cache of the platform, which avoids the heavy database intervention;(3) using the classical Levenshtein distance in the morphological similar words and semantic similar words recommendation algorithm. The platform implemented based on the above data and technology finally supports Japanese, Italian, Indonesian, Malaysian, Vietnamese, Filipino, and Myanmar seven languages. © 2023 ACM.

关键词： Websites

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Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinese 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Jingshen Chen, Xinglu Qiu, Xinying Wang, Zhimin Feng, Wenhe School of Information Science and Technology Guangzhou China Faculty of Chinese Language and Culture Guangzhou China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9780000000002

Chinese sentence simplification faces challenges due to the lack of large-scale labeled parallel corpora and the prevalence of idioms. To address these challenges, we propose Readability-guided Idiom-aware Sentence Simplification (RISS), a novel framework that combines data augmentation techniques. RISS introduces two key components: (1) Readability-guided Paraphrase Selection (RPS), a method for mining high-quality sentence pairs, and (2) Idiom-aware Simplification (IAS), a model that enhances the comprehension and simplification of idiomatic expressions. By integrating RPS and IAS using multi-stage and multi-task learning strategies, RISS outperforms previous state-of-the-art methods on two Chinese sentence simplification datasets. Furthermore, RISS achieves additional improvements when fine-tuned on a small labeled dataset. Our approach demonstrates the potential for more effective and accessible Chinese text simplification. © 2024 China National Conference on Computational Linguistics.

关键词： Computational linguistics

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Natural-Annotation-Based Malay Multiword Expressions Extraction and Clustering 19th

Natural-Annotation-Based Malay Multiword Expressions Extract...

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19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018

作者： Liu, Wuying Wang, Lin Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangdong Guangzhou510420 China Xianda College of Economics and Humanities Shanghai International Studies University Shanghai200083 China

ISBN: (纸本)9783031237928

Multiword expression (MWE) is an optimal granularity of language reuse. However, no explicit boundaries between MWEs and other words causes a serious problem on automatic identification of MWEs for some less commonly taught languages. This paper addresses the issue of Malay MWEs extraction and clustering, and proposes a novel unsupervised extraction and clustering algorithm based on natural annotations. In our algorithm, we firstly use a binary classification for each space character to solve length-varying Malay MWEs extraction, secondly transfer natural document-level category annotations to MWE-level ones for Malay MWEs clustering, and finally distill out a general MWEs resource and several domain resources. The experimental results in the Malay dataset of 272,783 text documents show that our algorithm can extract MWEs precisely and dispatch them into domain clusters efficiently. © 2023, Springer Nature Switzerland AG.

关键词： Extraction

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CNN-Based Broad Learning for Cross-Domain Emotion Classification

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Tsinghua Science and Technology 2023年第2期28卷 360-369页

作者： Rong Zeng Hongzhan Liu Sancheng Peng Lihong Cao Aimin Yang Chengqing Zong Guodong Zhou Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices South China Normal UniversityGuangzhou 511400China Laboratory of Language Engineering and Computing Guangdong University of Foreign StudiesGuangzhou 510006China School of Computer Science and Intelligence Education Lingnan Normal UniversityGuangzhou 510006China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer Science and Technology Soochow UniversitySuzhou 215031China

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 *** to the domain discrepancy,an emotion classifier trained on source domain may not work well on target *** researchers have focused on traditional cross-domain sentiment classification,which is coarse-grained emotion ***,the problem of emotion classification for cross-domain is rarely *** 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 *** first utilized CNN to extract domain-invariant and domain-specific features simultaneously,so as to train two more efficient classifiers by employing broad ***,to take advantage of these two classifiers,we designed a co-training model to boost together for ***,we conducted comparative experiments on four datasets for verifying the effectiveness of our proposed *** experimental results show that the proposed method can improve the performance of emotion classification more effectively than those baseline methods.

关键词： cross-domain emotion classification CNN broad learning classifier co-training

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Comparative Analysis of Para-social Interaction among Multiple Luxury Brands 4

Comparative Analysis of Para-social Interaction among Multip...

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4th International Conference on Natural language Processing, ICNLP 2022

作者： Yu, Dandan Liu, Wuying Ludong University Shandong Key Laboratory of Language Resources Development and Application Yantai264025 China Guangdong University of Foreign Studies Laboratory of Language Engineering and Computing Guangzhou510420 China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

The luxury industry is good at using the most advanced science and technology to promote product sales. With the recent rise of virtual spokespersons, more and more virtual spokespersons are widely used in luxury marketing. We studied the new advertising mode based on virtual spokespersons, and analyzed how luxury brands use virtual spokespersons to establish para-social interactions with their audiences. We also analyzed the data of likes, reposts and comments on the official Weibo of luxury brands such as Valentino, Dior, Ferragamo, Louis Vuitton, and March Jacobs that have published advertising Weibos which contain virtual spokespersons and do not include celebrity spokespersons and various marketing campaign. Finally, according to the conclusion of our analysis, we propose development suggestions for the promotion strategy of luxury brands' virtual spokespersons in Weibo or other media, and the way to establish para-social interaction between luxury brands and their audiences. © 2022 IEEE.

关键词： Marketing

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