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Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

作者： Xiaotian Zhang Chunyang Wu Hai Zhao Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong UniversityMOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University

ISBN: (纸本)9781627484046

We in this paper present the model for our participation (BCMI) in the CoNLL-2012 Shared Task. This paper describes a pure rule-based method, which assembles different filters in a proper order. Different filters handle different situations and the filtering strategies are designed manually. These filters are assigned to different ordered tiers from general to special cases. We participated in the Chinese and English closed tracks, scored 51.83 and 59.24 respectively.

关键词： Coreference Resolution Filtration Filters Ordered Chinese philosophy Chinese Chinese art proper order paper

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Hybrid Rule-based Algorithm for Coreference Resolution 12

Hybrid Rule-based Algorithm for Coreference Resolution

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Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

作者： Heming Shou Hai Zhao Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong UniversityMOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University

ISBN: (纸本)9781627484046

This paper describes our coreference resolution system for the CoNLL-2012 shared task. Our system is based on the Stanford's dcore-f deterministic system which applies multiple sieves with the order from high precision to low precision to generate coreference chains. We introduce the newly added constraints and sieves and discuss the improvement on the o-riginal system. We evaluate the system using OntoNotes data set and report our results of average F-score 58.25 in the closed track.

关键词： Coreference Resolution Sieves sieving

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Break index labeling of mandarin text via syntactic-to-prosodic tree mapping

Break index labeling of mandarin text via syntactic-to-proso...

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International Symposium on Chinese Spoken Language Processing

作者： Xiaotian Zhang Yao Qian Hai Zhao Frank K. Soong Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiaotong University Shanghai China Microsoft Research Asia Beijing China

In this study, we investigate the break index labeling problem with a syntactic-to-prosodic structure conversion. The statistical relationship between the mapped syntactic tree structure and prosodic tree structure of sentences in the training set is used to generate a Synchronous Tree Substitution Grammar (STSG) which can describe the probabilistic mapping (substitution) rules between them. For a given test sentence and the corresponding parsed syntactic tree structure, thus generated STSG can convert the syntactic tree to a prosodic tree statistically. We compare the labeling results with other approaches and show the probabilistic mapping can indeed benefit break index labeling performance.

关键词： IP networks Biological system modeling Pragmatics Abstracts Syntactics

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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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An immune local concentration based virus detection approach

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Journal of Zhejiang University-science C(computers and Electronics) 2011年第6期12卷 443-454页

作者： Wei WANG MOE Key Laboratory of Machine Perception Peking University Beijing 100871 China Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

Along with the evolution of computer viruses, the number of file samples that need to be analyzed has constantly increased. An automatic and robust tool is needed to classify the file samples quickly and efficiently. Inspired by the human immune system, we developed a local concentration based virus detection method, which connects a certain number of two-element local concentration vectors as a feature vector. In contrast to the existing data mining techniques, the new method does not remember exact file content for virus detection, but uses a non-signature paradigm, such that it can detect some previously unknown viruses and overcome the techniques like obfuscation to bypass signatures. This model first extracts the viral tendency of each fragment and identifies a set of statical structural detectors, and then uses an information-theoretic preprocessing to remove redundancy in the detectors’ set to generate ‘self’ and ‘nonself’ detector libraries. Finally, ‘self’ and ‘nonself’ local concentrations are constructed by using the libraries, to form a vector with an array of two elements of local concentrations for detecting viruses efficiently. Several standard data mining classifiers, including K -nearest neighbor (KNN), radial basis function (RBF) neural networks, and support vector machine (SVM), are leveraged to classify the local concentration vector as the feature of a benign or malicious program and to verify the effectiveness and robustness of this approach. Experimental results show that the proposed approach not only has a much faster speed, but also gives around 98% of accuracy.

关键词： Local concentration Artificial immune system Virus detection

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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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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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An intelligent multifeature statistical approach for the discrimination of driving conditions of a hybrid electric vehicle

An intelligent multifeature statistical approach for the dis...

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作者： Huang, Xi Tan, Ying He, Xingui Key Laboratory of Machine Perception and the Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing China

As a new kind of vehicle with low fuel cost and low emissions, the hybrid electric vehicle (HEV) has been paid much attention in recent years. The key technique in the HEV is adopting the optimal control strategy for the best performance. As the premise, correct driving condition discrimination has an extremely important significance. This paper proposes an intelligent multifeature statistical approach to automatically discriminate the driving condition of the HEV. First, this approach periodically samples the driving cycle. Then, it extracts multiple statistical features and tests their significance by statistical analysis to select effective features. Afterward, it applies a support vector machine (SVM) and other machine-learning methods to intelligently and automatically discriminate the driving conditions. Compared with others, the proposed approach can compute fast and discriminate in real time during the whole HEV running mode. In our experiments, it reaches an accuracy value of 95%. As a result, our approach can completely mine the valid information from the data and extract multiple features that have clear meanings and significance. Finally, according to the prediction experiment by a neural network, the fitting experiment by the autoregressive moving average model, and the simulation results of the control strategy, it turns out that our proposed approach raises the efficiency of considerably controlling the HEV. © 2006 IEEE.

关键词： Hybrid vehicles

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Reports of the AAAI 2012 conference workshops

Reports of the AAAI 2012 conference workshops

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作者： Agrawal, Vikas Baier, Jorge Bekris, Kostas Chen, Yiling D'Avila Garcez, Artur S. Hitzler, Pascal Haslum, Patrik Jannach, Dietmar Law, Edith Lecue, Freddy Lamb, Luis C. Matuszek, Cynthia Palacios, Hector Srivastava, Biplav Shastri, Lokendra Sturtevant, Nathan Stern, Roni Tellex, Stefanie Vassos, Stavros Center for Knowledge Driven Intelligent Systems Enterprise Technology Research Labs Infosys Limited India Pontificia Universidad Católica de Chile Chile Rutgers University NJ United States Harvard University United States City University London United Kingdom Wright State University Dayton OH United States Australian National University Australia TU Dortmund Germany Machine Learning Department Carnegie Mellon University United States IBM Research - Smarter Cities Technology Centre Dublin Ireland Federal University of Rio Grande do Sul Porto Alegre Brazil Computer Science and Engineering Department University of Washington United States Universidad Carlos III de Madrid Spain IBM Research - India India University of Denver United States Ben Gurion University Negev Israel Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece

The AAAI-12 Workshop program was held Sunday and Monday, July 22-23, 2012, at the Sheraton Centre Toronto Hotel in Toronto, Ontario, Canada. The AAAI-12 workshop program included nine workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages, Cognitive Robotics, Grounding Language for Physical Systems, Human Computation, Intelligent Techniques for Web Personalization and Recommendation Systems, Multiagent Pathfinding, Neural-Symbolic Learning and Reasoning, Problem Solving Using Classical Planners, and Semantic Cities. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial intelligence.

关键词： Problem solving

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Centroid integer selection model - A high efficiency method on dynamic multi-document summarization

Centroid integer selection model - A high efficiency method ...

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2011 International Conference on Asian Language Processing, IALP 2011

作者： Liu, Meiling Zheng, Dequan Zhao, Tiejun Yu, Yang Machine Intelligence and Translation Laboratory Harbin Institute of Technology Harbin China Department of Computer Science and Application Northeast Forestry University Harbin China

ISBN: (纸本)9780769545547

This paper researches centroid integer selection based on dynamic multi-document summarization (DMS) and presentes a dynamic multi-document summarization model, called Centroid Integer Selection Model (CISM). This model has mainly two steps. First, some abstracts were extracted from the document sets based on different first sentence, respectively. Second, the best abstract was selected based on centroid strategy from all the abstracts created in the first step. The best advantage this model showed was that it eliminated the effect caused by falsely selecting based on the first sentence. Some experiments were conducted on the Update Task test data from TAC2008, and results of new model were compared with results from the TAC2008 evaluation. © 2011 IEEE.

关键词： Abstracting

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