“111”基地Prof. Eduard Hovy参观PRIS实验室通知

        卡内基梅隆大学的Prof. Eduard Hovy将于后天下午(2013年12月11日(周三)下午14:30—17:00)到我们实验室参观讨论。Prof. Eduard Hovy是郭老师负责的111项目聘请的兼职教授,专门从事文本数据处理方面的研究,和我们的工作联系密切。建议大家都参加讨论学习。 

时间安排大致为:

14:30-15:30 请Prof. Eduard Hovy介绍在CMU的研究工作。

15:30-15:50 休息

15:50-17:00 介绍PRIS实验室工作情况。

 

参观人介绍:

Professor Eduard Hovy works at the Language Technologies Institute of Carnegie Mellon University. He is Co-Director for Research of the Command, Control, and Interoperability Center for Advanced Data Analysis (CCICADA). He was working at the University of Southern California, as a Fellow of its Information Sciences Institute (ISI), as Director of the Human Language Group, as Research Associate Professor in USC’s Computer Science Department, and as Director of Research for ISI’s Digital Government Research Center DGRC).

His research focuses on several topics around aspects of the computational semantics of human language, including text analysis, text summarization and generation, question answering, discourse and dialogue processing, ontologies, annotation, machine translation evaluation, and digital government.

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讲座通知——Web spam detection using machine learning techniques

澳门科技大学资讯科技学院院长蔡亚从教授(Prof. TSOI Ah-Chung)于6月3日来北邮作学术报告。欢迎感兴趣的同学老师踊跃参与。

讲座题目:Web spam detection using machine learning techniques

主讲人:TSOI Ah-Chung教授(澳门科技大学)

时间:2013年6月3日(星期一)下午14:30-16:30

地点:新办501会议室

报告摘要:Web spam detection is a challenging problem primarily because of the large number of features involved, the scarcity of having the number of validated spam sites, and that the data is related to one another through a web topology. In this talk, we will use machine learning techniques, e.g., self organising map, and multilayer perceptrons, except that in both cases, they are extended to handle graph data inputs, balancing of imbalanced data, feature reduction, to study the problem. We applied our techniques to two well known publicly available web spam detection datasets, namely, the UK 2006 dataset, and the UK 2007 dataset. On both datasets using our methods, we achieve the best generalisation results so far, published by others. Moreover our method can be applied to other graph based datasets, without much changes, e.g., the mutagenesis dataset, and we also achieve the best results so far. Hence, our techniques is a general methodology which can handle long term dependency in deep learning architectures.

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NIST TAC中KBP评测负责人季姮教授2013年6月7日访问实验室

NIST TAC中KBP评测负责人季姮教授将于2013年6月7日下午访问实验室,地点在718会议室,请感兴趣的同学参加。

季姮教授的简历如下:

Heng Ji is an assistant professor in Computer Science at Queens College, and a doctoral faculty member in the Computer Science Department and Linguistics Department at the Graduate Center of City University of New York. She received her Ph.D. in Computer Science from New York University in 2007. Her research interests focus on Natural Language Processing, especially on Cross-source Information Extraction and Knowledge Base Population. She has published over 90 papers. Her recent work on uncertainty reduction for Information Extraction was invited for publication in the Centennial Year Celebration of IEEE Proceedings. She received a Google Research Award in 2009, NSF CAREER award in 2010, Sloan Junior Faculty award and IBM Watson Faculty award in 2012. She served as the coordinator of the NIST TAC Knowledge Base Population task in 2010 and 2011, the Information Extraction area chair of NAACL-HLT2012 and ACL2013 and the co-leader of the information fusion task of ARL NS-CTA program in 2011 and 2012. Her research has been funded by NSF, ARL, DARPA, Google and IBM.

 

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2012NIST评测结果公布,PRIS实验室又获佳绩

2012年PRIS主要参加了NIST主办的TREC和TAC评测,主要成绩如下:

1、       TAC Knowledge Base Population (KBP) 2012

内容简介:

KBP评测的目标是促进自动化系统的研究,包括在大语料库中发现命名实体信息,以及将这些信息整合到知识库中。TAC 2012的任务有三块,均旨在提高从文本中自动填充知识库的能力:

Entity-Linking:给定一个query包含一个名字字符串,一个背景文档ID,一组标识字符串起始位置的UTF-8码,系统需要输出相应名字指示的KB entry的ID号,如果没有,输出一个”NILxxxx” ID。

Slot-Filling:给定一个命名实体、一个预定义的属性集,通过抓取相关值的信息,扩充一个KB节点的属性值。KB的参考文献来自英文维基。Slot Filler Validation这个诊断任务,将去判断参赛者的系统是否正确完成了填充。

Cold Start Knowledge Base Population:给定一个KB概要(空的知识库),通过挖掘大文本数据来构建出KB。

评测成绩简介

未命名

图1.KBP 2012 Slot-Filling前6名(PRIS名列第一)

未命名

图2. KBP 2012 Slot-Filling team(按年度,名次变化)

参加单位

伊利诺伊大学芝加哥分校、都柏林大学、德国萨尔大学、纽约大学、NEC Laboratories、广东外国语大学、香港理工大学、北京邮电大学、中国科学院等。

链接:

http://www.nist.gov/tac/2012/KBP/index.html

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学术报告通知: Bayesian Inference with Max-margin Posterior Regularization

清华大学计算机系朱军副教授将于11月23日上午来北邮作精彩学术报告。欢迎感兴趣的同学和老师踊跃参加。
 
报告题目: Bayesian Inference with Max-margin Posterior Regularization
报告人:朱军 副教授 清华大学智能技术与系统国家重点实验室智能媒体组
时间:2012年11月23日 10:00-11:30
地点:教三810会议室
 
报告摘要:Existing Bayesian models, especially nonparametric Bayesian methods, rely heavily on specially conceived priors to incorporate domain knowledge for discovering improved latent representations. While priors can affect posterior distributions through Bayes’ theorem, imposing posterior regularization is arguably more direct and in some cases can be more natural and easier. In this talk, I will present regularized Bayesian inference (RegBayes), a computational framework to perform posterior inference with a convex regularization on the desired post-data posterior distributions. When the convex regularization is induced from a linear operator on the posterior distributions, RegBayes can be solved with convex analysis theory. Furthermore, I will present some concrete examples, including MedLDA for learning discriminative topic representations and infinite latent support vector machines for learning discriminative latent features for classification. All these models explore the large-margin idea in combination with a (nonparametric) Bayesian model for discovering predictive latent representations. I will discuss both variational and Monte Carlo methods for approximate inference.
 
报告人简介:Dr. Jun Zhu is an associate professor in the Department of Computer Science and Technology at Tsinghua University. His principal research interests lie in the development of statistical machine learning methods for solving scientific and engineering problems arising from artificial and biological learning, reasoning, and decision-making in the high-dimensional and dynamic worlds. Prof. Zhu received his Ph.D. in Computer Science from Tsinghua University, and his advisor was Prof. Bo Zhang. He did post-doctoral research with Prof. Eric P. Xing in the Machine Learning Department at Carnegie Mellon University. His current work involves both the foundations of statistical learning, including theory and algorithms for probabilistic latent variable models, sparse learning in high dimensions, Bayesian nonparametrics, and large-margin learning; and the application of statistical learning in social network analysis, data mining, and multi-media data analysis.

伦敦大学玛丽女王学院Shaogang Gong教授访问实验室

9月24日,应实验室郭军老师的邀请,英国伦敦大学玛丽女王学院Shaogang Gong教授访问实验室,并受聘为我校高等智能与网络服务创新引智基地(111基地)长期高级访问科学家,授予仪式在行政办公楼501会议室举行。我校高等智能与网络服务创新引智基地主任、副校长郭军教授出席了授予仪式并在授予仪式前亲切会见了Gong教授及陪同访问的英国伦敦大学玛丽女王学院Tao Xiang博士和Yizhe Song博士。国际处任春霞处长,研究生院副院长、国际学院副院长张琳教授,高等智能与网络服务创新引智基地秘书王小捷教授,信息与通信工程学院杨洁副院长、及实验室的部分师生参加了授予仪式。

授予仪式由高等智能与网络服务创新引智基地王小捷教授主持。郭军副校长代表高等智能与网络服务创新引智基地致词,对Gong教授受聘为我校高等智能与网络服务创新引智基地长期高级访问科学家表示祝贺,并希望双方未来能够有一个良好的合作前景。张洪刚老师介绍了Gong教授的履历。随后,郭军副校长为Gong教授颁发聘书并佩戴校徽。Gong教授对北京邮电大学给予他这个殊荣表示了由衷的感谢。接下来,Gong教授作了题为“From Surveillance to Big Data”的专题学术报告,受到参会师生的热烈欢迎。

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学术报告通知:Rank Minimization: A Way to 2D Sparsity

应我校信息与通信工程学院模式识别与智能系统实验室郭军教授邀请,微软亚洲研究院主管研究员林宙辰博士将于2010年12月22日来我校作精彩学术报告。欢迎广大师生踊跃参加。

 

报告题目: Rank Minimization: A Way to 2D Sparsity

报告人:林宙辰博士  微软亚洲研究院视觉计算组主管研究员(Lead Researcher)

时间:2010年12月22日 15:00-16:30

地点:教三楼 411

 

报告摘要:Sparse representation has been a hot topic in signal processing and machine learning in recent years. In the past, people usually discussed 1D sparsity, i.e., the number of nonzeros in a vector. For 2D signals, we can actually also define a measure of sparsity, namely the rank of the data matrix. In this talk, I will give a brief history of sparse representation and then introduce some core problems and theories of rank minimization. Finally, I will show some applications of rank minimization that have resulted in improved performance in handling imperfect data that can have noise, outliers and missing values.

 

报告人简介:Dr. Zhouchen Lin is a Lead Researcher at Visual Computing Group, Microsoft Research Asia. He received the Ph.D. degree in applied mathematics from PekingUniversity in 2000. He is now a guest professor to ShanghaiJiaotongUniversity, BeijingJiaotongUniversity and SoutheastUniversity. He is also a guest researcher to Institute of Computing Technology, ChineseAcademy of Sciences. His research interests include computer vision, image processing, computer graphics, machine learning, pattern recognition, and numerical computation and optimization. He is a senior member of the IEEE.