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Hierarchical Network with Label Embedding for Contextual Emotion Recognition

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Research 2021年第1期2021卷 103-111页

作者： Jiawen Deng fuji ren Faculty of Engineering Tokushima UniversityTokushima 770-8506Japan

Emotion recognition has been used widely in various applications such as mental health monitoring and emotional ***,emotion recognition is regarded as a text classification *** recognition is a more complex problem,and the relations of emotions expressed in a text are *** this paper,a hierarchical model with label embedding is proposed for contextual emotion ***,a hierarchical model is utilized to learn the emotional representation of a given sentence based on its contextual *** give emotion correlation-based recognition,a label embedding matrix is trained by joint learning,which contributes to the final *** experiments are conducted on Chinese emotional corpus renCECps,and the experimental results indicate that our approach has a satisfying performance in textual emotion recognition task.

关键词： emotional contextual has

CERG:Chinese Emotional Response Generator with Retrieval Method

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Research 2020年第1期2020卷 404-411页

作者： Yangyang Zhou fuji ren Faculty of Engineer University of TokushimaTokushima 770-8506Japan

The dialogue system has always been one of the important topics in the domain of artificial *** far,most of the mature dialogue systems are task-oriented based,while non-task-oriented dialogue systems still have a lot of room for *** propose a data-driven non-task-oriented dialogue generator“CERG”based on neural *** model has the emotion recognition capability and can generate corresponding *** data set we adopt comes from the NTCIR-14 STC-3 CECG subtask,which contains more than 1.7 million Chinese Weibo post-response pairs and 6 emotion *** try to concatenate the post and the response with the emotion,then mask the response part of the input text character by character to emulate the encoder-decoder *** use the improved transformer blocks as the core to build the model and add regularization methods to alleviate the problems of overcorrection and exposure *** introduce the retrieval method to the inference process to improve the semantic relevance of generated *** results of the manual evaluation show that our proposed model can make different responses to different emotions to improve the human-computer interaction *** model can be applied to lots of domains,such as automatic reply robots of social application.

关键词： computer artificial character

Exploring Latent Semantic Information for Textual Emotion Recognition in Blog Articles

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 204-216页

作者： Xin Kang fuji ren Yunong Wu Faculty of Engineering Tokushima University 2-1 Minamijyousanjima-cho Tokushima 770-8506 Japan IEEE

Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things(Io T). The major difficulty is caused by the lack of basic knowledge in emotion expressions with respect to a variety of real world contexts. In this paper, we propose a Bayesian inference method to explore the latent semantic dimensions as contextual information in natural language and to learn the knowledge of emotion expressions based on these semantic dimensions. Our method synchronously infers the latent semantic dimensions as topics in words and predicts the emotion labels in both word-level and document-level texts. The Bayesian inference results enable us to visualize the connection between words and emotions with respect to different semantic dimensions. And by further incorporating a corpus-level hierarchy in the document emotion distribution assumption, we could balance the document emotion recognition results and achieve even better word and document emotion predictions. Our experiment of the wordlevel and the document-level emotion predictions, based on a well-developed Chinese emotion corpus ren-CECps, renders both higher accuracy and better robustness in the word-level and the document-level emotion predictions compared to the state-of-theart emotion prediction algorithms.

关键词： Bayesian inference emotion-topic model emotion recognition multi-label classification natural language understanding

Differently implicational α-universal triple I restriction method of (1, 2, 2) type

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Journal of Systems Engineering and Electronics 2012年第4期23卷 560-573页

作者： Yiming Tang fuji ren Yanxiang Chen Information and Communication Engineering Postdoctoral Research Station Hefei University of TechnologyHefei 230009P.R.China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191P.R.China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of TechnologyHefei 230009P.R.China Institute of Technology and Science The University of TokushimaMinami Josanjima770-8506Japan

From the viewpoints of both fuzzy system and fuzzy reasoning, a new fuzzy reasoning method which contains the α- triple I restriction method as its particular case is proposed. The previous α-triple I restriction principles are improved, and then the optimal restriction solutions of this new method are achieved, especially for seven familiar implications. As its special case, the corresponding results of α-triple I restriction method are obtained and improved. Lastly, it is found by examples that this new method is more reasonable than the α-triple I restriction method.

关键词： fuzzy reasoning fuzzy system triple I method triple Irestriction method compositional rule of inference method.

DFF-ResNet: An Insect Pest Recognition Model Based on Residual Networks

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Big Data Mining and Analytics 2020年第4期3卷 300-310页

作者： Wenjie Liu Guoqing Wu fuji ren Xin Kang the School of Information Science and Technology and School of Transportation and Civil Engineering Nantong UniversityNantong 226019China the Faculty of Engineering Tokushima UniversityTokushima 770-8506Japan the School of Information Science and Technology Nantong UniversityNantong 226019China

Insect pest control is considered as a significant factor in the yield of commercial ***,to avoid economic losses,we need a valid method for insect pest *** this paper,we proposed a feature fusion residual block to perform the insect pest recognition *** on the original residual block,we fused the feature from a previous layer between two 11 convolution layers in a residual signal branch to improve the capacity of the ***,we explored the contribution of each residual group to the model *** found that adding the residual blocks of earlier residual groups promotes the model performance significantly,which improves the capacity of generalization of the *** stacking the feature fusion residual block,we constructed the Deep Feature Fusion Residual Network(DFF-ResNet).To prove the validity and adaptivity of our approach,we constructed it with two common residual networks(Pre-ResNet and Wide Residual Network(WRN))and validated these models on the Canadian Institute For Advanced Research(CIFAR)and Street View House Number(SVHN)benchmark *** experimental results indicate that our models have a lower test error than those of baseline ***,we applied our models to recognize insect pests and obtained validity on the IP102 benchmark *** experimental results show that our models outperform the original ResNet and other state-of-the-art methods.

关键词： insect pest recognition deep feature fusion residual network image classification

Slang feature extraction by analysing topic change on social media

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CAAI Transactions on Intelligence Technology 2019年第1期4卷 64-71页

作者： Kazuyuki Matsumoto fuji ren Masaya Matsuoka Minoru Yoshida Kenji Kita Graduate School of Technology Industrial and Social SciencesTokushima University770-8506Tokushima-shiMinamijosanjima-cho 2-1Japan

Recently,the authors often see words such as youth slang,neologism and Internet slang on social networking sites(SNSs)that are not registered on *** the documents posted to SNSs include a lot of fresh information,they are thought to be useful for collecting *** is important to analyse these words(hereinafter referred to as‘slang’)and capture their features for the improvement of the accuracy of automatic information *** study aims to analyse what features can be observed in slang by focusing on the *** construct topic models from document groups including target slang on Twitter by latent Dirichlet *** the models,they chronologically the analyse change of topics during a certain period of time to find out the difference in the features between slang and general ***,they propose a slang classification method based on the change of features.

关键词： SNSs social media

Message from the Guest Editor for the Special Issue

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China Communications 2012年第3期9卷 I0002-I0003页

作者： ren fuji the Faculty of Engineering of the University of Tokushima Japan the International Advanced Information Institute Chinese Association of Artificial Intelligence

Prof. ren fuji received his B.E. and M.E. degrees from Beijing University of Posts and Telecommunications, Beijing, China, in 1982 and 1985, respectively. He received his Ph.D. degree in 1991 from Hokkaido University, Japan. He is a professor in the Faculty of Engineering of the University of Tokushima, Japan. His research interests include natural language processing, artificial intelligence, language understanding and communication, and affective computing. He is a member of IEICE, CAAI, IEEJ, IPSJ, JSAI, AAMT, and IEEE. He is a fellow of the Japan Federation of Engineering Societies. He is the president of the International Advanced Information Institute and Vice Presidentof Chinese Association of Artificial Intelligence.

关键词：日本北海道大学北京邮电大学自然语言处理编辑消息人工智能工程学会工程学院

Enhancing Offensive Language Detection with Data Augmentation and Knowledge Distillation

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Research 2024年第2期6卷 29-40页

作者： Jiawen Deng Zhuang Chen Hao Sun Zhexin Zhang Jincenzi Wu Satoshi Nakagawa fuji ren Minlie Huang The CoAl group DCSTInstitute for Artificial InteligenceState Key Lab of Intelligent Technology and SystemsBeijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China Graduate School of Information Science&Technology The University of Tokyo.Tokyo 1138654Japan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengduSichuan611731China

Offensive language detection has received important attention and plays a crucial role in promoting healthy communication on social platforms,as well as promoting the safe deployment of large language *** data is the basis for developing detectors;however,the available offense-related dataset in Chinese is severely limited in terms of data scale and coverage when compared to English *** significantly affects the accuracy of Chinese offensive language detectors in practical applications,especially when dealing with hard cases or out-of-domain *** alleviate the limitations posed by available datasets,we introduce AugCOLD(Augmented Chinese Offensive Language Dataset),a large-scale unsupervised dataset containing 1 million samples gathered by data crawling and model ***,we employ a multiteacher distillation framework to enhance detection performance with unsupervised *** is,we build multiple teachers with publicly accessible datasets and use them to assign soft labels to *** soft labels serve as a bridge for knowledge to be distilled from both AugCOLD and multiteacher to the student network,i.e.,the final offensive *** conduct experiments on multiple public test sets and our well-designed hard tests,demonstrating that our proposal can effectively improve the generalization and robustness of the offensive language detector.

关键词： COLD dealing limitations

Predicting Complex Word Emotions and Topics through a Hierarchical Bayesian Network

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China Communications 2012年第3期9卷 99-109页

作者： Kang Xin ren fuji Univ Tokushima Fac Engn Tokushima 7708501 Japan Hefei Univ Technol AIM Hefei 230009 Peoples R China

In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories： joy, love, expectation, sur- prise, anxiety, sorrow, anger and hate. We use a hi- erarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without con- sidering any complicated language features. Our ex- periment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields（CRFs） on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.

关键词： word emotion classification complex e-motion emotion intensity prediction emotion-topicvariation hierarchical Bayesian network