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2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014

作者： Zhang, Jingyi Utiyama, Masao Sumita, Eiichro Zhao, Hai Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence Shanghai Jiao Tong Unviersity Shanghai200240 China Key Laboratory Shanghai Education Commission Intelligent Interaction Cognitive Engineering Shanghai Jiao Tong Unviersity Shanghai200240 China National Institute of Information and Communications Technology 3-5Hikaridai Keihanna Science City Kyoto619-0289 Japan

ISBN: (纸本)9781937284961

We propose a simple and effective approach to learn translation spans for the hierarchical phrase-based translation model. Our model evaluates if a source span should be covered by translation rules during decoding, which is integrated into the translation system as soft constraints. Compared to syntactic constraints, our model is directly acquired from an aligned parallel corpus and does not require parsers. Rich source side contextual features and advanced machine learning methods were utilized for this learning task. The proposed approach was evaluated on NTCIR-9 Chinese-English and Japanese-English translation tasks and showed significant improvement over the baseline system. © 2014 Association for Computational Linguistics.

关键词： Syntactics

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Hedge detection and scope finding by sequence labeling with normalized feature selection

CoNLL-2010: Shared Task - Fourteenth Conference on Computat...

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CoNLL-2010: Shared Task - Fourteenth Conference on Computational Natural Language Learning, Proceedings of the Shared Task 2010年 92-99页

作者： Zhang, Shaodian Zhao, Hai Zhou, Guodong Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Dept of Computer Science and Engineering Shanghai Jiao Tong University China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University China School of Computer Science and Technology Soochow University China

This paper presents a system which adopts a standard sequence labeling technique for hedge detection and scope finding. For the first task, hedge detection, we formulate it as a hedge labeling problem, while for the second task, we use a two-step labeling strategy, one for hedge cue labeling and the other for scope finding. In particular, various kinds of syntactic features are systemically exploited and effectively integrated using a large-scale normalized feature selection method. Evaluation on the CoNLL-2010 shared task shows that our system achieves stable and competitive results for all the closed tasks. Furthermore, post-deadline experiments show that the performance can be much further improved using a sufficient feature selection. © 2010 Association for Computational Linguistics.

关键词： Feature Selection

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Neural network based bilingual language model growing for statistical machine translation

Neural network based bilingual language model growing for st...

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2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014

作者： Wang, Rui Zhao, Hai Lu, Bao-Liang Utiyama, Masao Sumita, Eiichro Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence Shanghai Jiao Tong University Shanghai200240 China Multilingual Translation Laboratory MASTAR Project National Institute of Information and Communications Technology 3-5 Hikaridai Keihanna Science City Kyoto619-0289 Japan Key Laboratory Shanghai Education Commission Intelligent Interaction Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781937284961

Since larger n-gram Language Model (LM) usually performs better in Statistical machine Translation (SMT), how to construct efficient large LM is an important topic in SMT. However, most of the existing LM growing methods need an extra monolingual corpus, where additional LM adaption technology is necessary. In this paper, we propose a novel neural network based bilingual LM growing method, only using the bilingual parallel corpus in SMT. The results show that our method can improve both the perplexity score for LM evaluation and BLEU score for SMT, and significantly outperforms the existing LM growing methods without extra corpus. © 2014 Association for Computational Linguistics.

关键词： Neural networks

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An Empirical Comparative Study on Two Large-Scale Hierarchical Text Classification Approaches

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International Journal of Computer Processing of Languages 2011年第4期23卷 309-325页

作者： JIAN ZHANG HAI ZHAO LIQING ZHANG BAO-LIANG LU Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiaotong University 800 Dongchuan Rd. Shanghai 200240 China Department of Computer Science Virginia Tech Blacksburg VA 24061 USA

Patent classification is a large scale hierarchical text classification (LSHTC) task. Though comprehensive comparisons, either learning algorithms or feature selection strategies, have been fully made in the text categorization field, few work was done for a LSHTC task due to high computational cost and complicated structural label characteristics. For the first time, this paper compares two popular learning frameworks, namely, hierarchical support vector machine (SVM) and k -nearest neighbor ( k -NN) that are applied to a LSHTC task. Our experimental results show that the latter outperforms the former for the LSHTC task, which is quite different from the existing results for normal text categorization tasks. In addition, this paper compares different similarity measures and ranking strategies in k -NN framework for LSHTC task. From our empirical study, conclusions can be drawn that k -NN is more appropriate for the LSHTC task than hierarchical SVM. BM25 outperforms other similarity measures and ListWeak gains a better performance than other ranking strategies. Our empirical results also indicate that using all the labels of the retrieved neighbors can remarkably improve classification performance over only using the first label of the retrieved neighbors.

关键词： Large-scale text classification hierarchical text classification text classification k -NN hierarchical SVM similarity measure ranking strategies

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EEG-based fatigue classification by using parallel hidden Markov model and pattern classifier combination

EEG-based fatigue classification by using parallel hidden Ma...

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19th International Conference on Neural Information Processing, ICONIP 2012

作者： Sun, Hui Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China MOE Key Laboratory of Systems Biomedicine Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China

ISBN: (纸本)9783642344770

Fatigue is the most important reason leading to traffic accidents. In order to ensure traffic safety, various methods based on electroencephalogram (EEG) are proposed. But most of them, either regression or classification, are focused on the relationship between feature space and observation values, so the changing patterns of features are ignored or discarded. In this paper, we propose a new fatigue classification method by using parallel hidden-Markov-model and pattern classifier combination techniques, where each model represents a particular fatigue-high-related feature. In the experiment, subjects are asked to accomplish some simple tasks, and both their fatigue states and their EEG signals are recorded simultaneously. Experimental results indicate that the mean error rate obtained by using our new method are 11.15% for classifying 3 states and 16.91% for classifying 4 states, respectively, while the existing approach can only reach 16.45% and 23.55% under the same condition. © 2012 Springer-Verlag.

关键词： Electroencephalography

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EEG-based emotion recognition in listening music by using support vector machine and linear dynamic system

EEG-based emotion recognition in listening music by using su...

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19th International Conference on Neural Information Processing, ICONIP 2012

作者： Duan, Ruo-Nan Wang, Xiao-Wei Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China MOE Key Laboratory of Systems Biomedicine Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China

ISBN: (纸本)9783642344770

This paper focuses on the variation of EEG at different emotional states. We use pure music segments as stimuli to evoke the exciting or relaxing emotions of subjects. EEG power spectrum is adopted to form features, power spectrum, differential asymmetry, and rational asymmetry. A linear dynamic system approach is applied to smooth the feature sequence. Minimal-redundancy-maximal-relevance algorithm and principal component analysis are used to reduce the dimension of features. We evaluate the performance of support vector machine, k-nearest neighbor classifiers and least-squares classifiers. The accuracy of our proposed method reaches 81.03% on average. And we show that the frequency bands, beta and theta, perform better than other frequency bands in the task of emotion recognition. © 2012 Springer-Verlag.

关键词： Emotion Recognition

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Online vigilance analysis combining video and electrooculography features

Online vigilance analysis combining video and electrooculogr...

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19th International Conference on Neural Information Processing, ICONIP 2012

作者： Du, Ruo-Fei Liu, Ren-Jie Wu, Tian-Xiang Lu, Bao-Liang Center for Brain-like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China MOE Key Laboratory of Systems Biomedicine Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China

ISBN: (纸本)9783642344992

In this paper, we propose a novel system to analyze vigilance level combining both video and Electrooculography (EOG) features. For one thing, the video features extracted from an infrared camera include percentage of closure (PERCLOS) and eye blinks, slow eye movement (SEM), rapid eye movement (REM) are also extracted from EOG signals. For another, other features like yawn frequency, body posture and face orientation are extracted from the video by using Active Shape Model (ASM). The results of our experiments indicate that our approach outperforms the existing approaches based on either video or EOG merely. In addition, the prediction offered by our model is in close proximity to the actual error rate of the subject. We firmly believe that this method can be widely applied to prevent accidents like fatigued driving in the future. © 2012 Springer-Verlag.

关键词： Eye movements

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Sleep Quality Estimation with Adversarial Domain Adaptation: From Laboratory to Real Scenario

Sleep Quality Estimation with Adversarial Domain Adaptation:...

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International Joint Conference on Neural Networks

作者： Jia-Jun Tong Yun Luo Bo-Qun Ma Wei-Long Zheng Bao-Liang Lu Xiao-Qi Song Shi-Wei Ma Department of Computer Science and Engineering Center for Brain-like Computing and Machine Intelligence Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China China Railway Lanzhou Group Co. Ltd. Gansu China China Academy of Railway Sciences Beijing China

Previous studies on EEG-based last-night sleep quality estimation mainly focus on evaluation with data from laboratory experiments. However, due to the reality gap constituted with device performance, subject groups, experiment settings and controlled conditions, the models trained solely on laboratory data cannot generalize well to real scenarios. In this work, we investigate the sleep quality estimation for high-speed train drivers as an instance of real-scenario application. Domain adaptation models are adopted to deal with the individual differences across subjects when modeling and testing with the real-scenario data. As it is usually difficult and costly to acquire data and annotate them in real scenarios, the high-quality data in laboratory conditions are used for model trainings. Knowledge from simulation is transferred to reality with domain adaptation methods. A novel approach called Domain Adversarial Neural Network (DANN) is adopted. DANN learns domain independent features through deep networks with an adversarial architecture. The experimental results indicate that DANN outperforms other state-of-the-art methods and achieves 19.55% and 23.50% improvements in terms of accuracy on the cross-subject and cross-scenario tasks, respectively, in comparison with the baseline SVM model.

关键词： Sleep Electroencephalography Feature extraction brain modeling Adaptation models Estimation Data models

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Emotion Recognition under Sleep Deprivation Using a Multimodal Residual LSTM Network

Emotion Recognition under Sleep Deprivation Using a Multimod...

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International Joint Conference on Neural Networks (IJCNN)

作者： Le-Yan Tao Bao-Liang Lu Department of Computer Science and Engineering Center for Brain-like Computing and Machine Intelligence Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Brain Science and Technology Research Center Shanghai China Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Emotion recognition under sleep deprivation is instructive for the study of mental disorders such as major depressive disorder. Previous studies on emotion recognition under sleep deprivation have been mainly based on psychological research techniques. In this paper, we introduce a multilayer weight-sharing multimodal residual LSTM network for emotion recognition under sleep deprivation. The advantage of our proposed method is that it allows for the combination of three different features: the electroencephalography (EEG) single-channel differential entropy (DE) features, EEG functional strength features with topological correlation connectivity, and eye movement features. The experiments under the conditions of sleep deprivation, sleep recovery and baseline are designed and conducted. The experimental results demonstrate that the proposed method significantly enhances the performance compared with the simple concatenation of the features of different modalities, and the best mean accuracies of 86.86% and 82.03% are achieved for four emotions (happiness, sadness, fear, and neutral) in subject-dependent and cross-subject emotion recognition tasks under 30 hours of sleep deprivation, respectively. The classification accuracy of the happiness emotion is obviously impaired under sleep deprivation, indicating that sleep deprivation impairs the stimulation of the happiness emotion, and one night of sleep recovery can reactivate the elicitation of the happiness emotion to the baseline level. Furthermore, we study the brain neural patterns of the four emotional states. The prefrontal area becomes less activated for the happiness emotion and sadness emotion in the gamma band under sleep deprivation, while the neural pattern of the fear emotion is highly robust with respect to sleep deprivation.

关键词： Feature extraction Electroencephalography Sleep Emotion recognition Correlation Physiology Task analysis

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Cross-Subject Decision Confidence Estimation from EEG Signals Using Spectral-Spatial-Temporal Adaptive GCN with Domain Adaptation

Cross-Subject Decision Confidence Estimation from EEG Signal...

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International Joint Conference on Neural Networks (IJCNN)

作者： Rong-Fei Gu Rui Li Wei-Long Zheng Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

The study of the human decision-making process has long been a valuable field for both scientific research and practical application. Towards knowing and taking control of the decision-making process, evaluating the reliability of human decisions objectively plays an important role. Various studies have demonstrated that the confidence level of humans during the decision-making process is an important factor that reflects the correctness of decisions. In literature, several deep learning based methods have been developed to estimate decision confidence using Electroencephalography (EEG). Among these approaches, the spectral-spatial-temporal adaptive graph convolutional neural network (SST-AGCN) stands out. However, SST-AGCN focuses on specific subjects, and may lead to less efficiency in cross-subject situations, which are more common in application scenarios. In this paper, we propose a deep learning model called SST-AGCN with Domain Adaptation (SST-AGCN-DA) for cross-subject decision confidence estimation. To examine the effectiveness of our proposed model, we compare our SST-AGCN-DA with the original SST-AGCN, three typical domain adaption algorithms in the field, and the SST-AGCN with Domain Generalization (SST-AGCN-DG), which is another transfer learning model we developed in this paper. We conduct cross-subject confidence estimation experiments on an EEG dataset collected under a text-based decision-making task. The averaged results of leave-one-out cross-validation come out that the F1-scores of our proposed SST-AGCN-DA and SST-AGCN-DG are 79.45% and 77.04%, respectively, while the original SST-AGCN and the best of the existing domain adaptation algorithms are 74.15% and 74.25%, respectively.

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