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An event summarizing algorithm based on the timeline relevance model in Sina Weibo

Science China(Information Sciences) 2018年第12期61卷 184-186页

作者： Kai LEI Lizhu ZHANG Ying LIU Ying SHEN Chenwei LIU Qian YU Weitao WENG Shenzhen Key Lab for Cloud Computing Technology & Applications (SPCCTA) School of Electronics and Computer Engineering Peking University

Dear editor,Depicting superior punctuality and originality,Weibo has become increasingly critical and influential in China for online information acquisition and sharing. However, very few research has studied Weibo to investigate event summarizing even though most of the published Weibos are event-driven. Besides, we observe that the existing methods are unsuitable for process-

关键词： IDF TF HAC An event summarizing algorithm based on the timeline relevance model in Sina Weibo

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TopicDSDR: Combining topic decomposition and data reconstruction for summarization

TopicDSDR: Combining topic decomposition and data reconstruc...

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14th International Conference on Web-Age Information Management, WAIM 2013

作者： Zhang, Zhiming Li, Hongjie Huang, Lian'En Shenzhen Key Lab for Cloud Computing Technology and Applications Peking University Shenzhen Graduate School Shenzhen Guangdong China

ISBN: (纸本)9783642385612

Multi-document summarization attempts to select the most important information to generate a compressed summary from a collection of documents. From the perspective of data reconstruction, a good summary may also well reconstruct the original documents. A document generally contains a variety of information centered around a main topic and covers different aspects of the main topic. In this paper we propose a novel model that combines data reconstruction and topic decomposition to summarize the documents, named TopicDSDR, which can not only best reconstruct the original documents but also capture the semantic similarity and main topics. We discuss two kinds of reconstructions: linear reconstruction and nonnegative reconstruction. We use the generalized Kullback-Leibler(KL) divergence as the loss function to evaluate the quality of summary for linear and nonnegative reconstruction and develop two new algorithms respectively. We conduct experiments on DUC2006 and DUC2007 summarization data sets, the experimental results demonstrate the effectiveness of our proposed methods. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Semantics

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Dynamic cache partitioning based on hot page migration

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Frontiers of Computer Science 2012年第4期6卷 363-372页

作者： Xiaolin WANG Xiang WEN Yechen LI Zhenlin WANG Yingwei LUO Xiaoming LI Department of Computer Science Peking UniversityBeijing 100871China Department of Computer Science Michigan Technological UniversityHoughtonMI 49931USA The Shenzhen Key Lab for Cloud Computing Technology and Applications Peking University Shenzhen Graduate SchoolShenzhen 518055China

Static cache partitioning can reduce inter- application cache interference and improve the composite performance of a cache-polluted application and a cache- sensitive application when they run on cores that share the last level cache in the same multi-core processor. In a virtu- alized system, since different applications might run on dif- ferent virtual machines （VMs） in different time, it is inappli- cable to partition the cache statically in advance. This paper proposes a dynamic cache partitioning scheme that makes use of hot page detection and page migration to improve the com- posite performance of co-hosted virtual machines dynami- cally according to prior knowledge of cache-sensitive appli- cations. Experimental results show that the overhead of our page migration scheme is low, while in most cases, the com- posite performance is an improvement over free composition.

关键词： cache partitioning hot page migration dynamic cache partitioning virtualization virtual machine monitor

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Comments-oriented document summarization based on multi-aspect co-feedback ranking

Comments-oriented document summarization based on multi-aspe...

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14th International Conference on Web-Age Information Management, WAIM 2013

作者： Huang, Lifu Li, Hongjie Huang, Lian'En Shenzhen Key Lab for Cloud Computing Technology and Applications Peking University Shenzhen Graduate School Shenzhen Guangdong 518055 China

ISBN: (纸本)9783642385612

With the popularity of Web 2.0, comments left by readers on web documents have drawn much attention. In this paper, we study the problem of comments-oriented document summarization, which aims to summarize a web document by considering not only its content but also the comments. Generally, most of the comments usually convey one or a few aspects of the document. Given a sentence set from both the web document and its corresponding comments to summarize, we can divide different sentences into different clusters (named "aspects") according to the content. It is challenging and interesting to summarize the web document based on these clusters. Motivated by this, we propose a novel model: MultiAspectCoRank, for comments-oriented document summarization. Firstly we rank all the sentences based on the multiple aspects obtained from the whole document, and then provide each ranking list as feedback to others until the top-N results of each ranking list are unchanged. We get the final result by integrating these different ranking lists together. Experimental results on a set of real-world blog data with manually labeled sentences show the promising performance of our approach. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Computers

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Optimized event storyline generation based on mixture-event-aspect model

Optimized event storyline generation based on mixture-event-...

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2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013

作者： Huang, Lifu Huang, Lian'en Shenzhen Key Lab for Cloud Computing Technology and Applications Peking University Shenzhen Graduate School Shenzhen Guangdong518055 China

ISBN: (纸本)9781937284978

Recently, much research focuses on event storyline generation, which aims to produce a concise, global and temporal event summary from a collection of articles. Generally, each event contains multiple sub-events and the storyline should be composed by the component summaries of all the sub-events. However, different sub-events have different part-whole relationship with the major event, which is important to correspond to users' interests but seldom considered in previous work. To distinguish different types of sub-events, we propose a mixture-event-aspect model which models different sub-events into local and global aspects. Combining these local/global aspects with summarization requirements together, we utilize an optimization method to generate the component summaries along the timeline. We develop experimental systems on 6 distinctively different datasets. Evaluation and comparison results indicate the effectiveness of our proposed method. © 2013 Association for Computational Linguistics.

关键词： Mixtures

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Automatic multi-document summarization based on new sentence similarity measures

Automatic multi-document summarization based on new sentence...

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12th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2012

作者： Yin, Wenpeng Pei, Yulong Huang, Lian'en Shenzhen Key Lab. for Cloud Computing Technology and Applications Peking University Shenzhen Graduate School Shenzhen Guangdong 518055 China

ISBN: (纸本)9783642326943

The acquiring of sentence similarity has become a crucial step in graph-based multi-document summarization algorithms which have been intensively studied during the past decade. Previous algorithms generally considered sentence-level structure information and semantic similarity separately, which, consequently, had no access to grab similarity information comprehensively. In this paper, we present a general framework to exemplify how to combine the two factors above together so as to derive a corpus-oriented and more discriminative sentence similarity. Experimental results on the DUC2004 dataset demonstrate that our approaches could improve the multi-document summarization performance to a considerable extent. © 2012 Springer-Verlag.

关键词： Semantics

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Generic multi-document summarization using topic-oriented information

Generic multi-document summarization using topic-oriented in...

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12th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2012

作者： Pei, Yulong Yin, Wenpeng Huang, Lian'En Shenzhen Key Lab. for Cloud Computing Technology and Applications Peking University Shenzhen Graduate School Shenzhen Guangdong 518055 China

ISBN: (纸本)9783642326943

The graph-based ranking models have been widely used for multi-document summarization recently. By utilizing the correlations between sentences, the salient sentences can be extracted according to the ranking scores. However, sentences are treated in a uniform way without considering the topic-level information in traditional methods. This paper proposes the topic-oriented PageRank (ToPageRank) model, in which topic information is fully incorporated, and the topic-oriented HITS (ToHITS) model is designed to compare the influence of different graph-based algorithms. We choose the DUC2004 data set to examine the models. Experimental results demonstrate the effectiveness of ToPageRank. And the results also show that ToPageRank is more effective and robust than other models including ToHIST under different evaluation metrics. © 2012 Springer-Verlag.

关键词： Graphic methods

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A method to evaluate the spatial extensibility of a switching unit and network

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Science China(Information Sciences) 2014年第2期57卷 140-156页

作者： ZHANG Bo WU JunTing WANG BinQiang LI Hui National Digital Switching System Engineering Technological R&D Center Shenzhen Key Lab of Cloud Computing Technology and Application

Switching units and networks have been analyzed as extensible fabrics,mostly in terms of their scheduling *** traditional literature on switching extensibility has provided complexity theory only relating to the total numbers of inputs(or outputs)and exchange *** paper analyzes switching extensibility in terms of not only the scheduling algorithm and also the fabric *** is found that determining extensibility from soft complexity related to the number of inputs(or outputs)of the scheduling algorithm and the fabric extensibility in previous studies without quantization is a flawed conception.A method is thus proposed to express the spatial extensibility of a switching unit or network in terms of the connections of a switching resource and *** method calculates parameter ES(the efciency of switching)of an m×n switching unit and obtains two functions of the switching unit to describe spatial extensibility along with the number of unilateral inputs or *** is found that the range of ES is(0,1]and three types of switching unit and two types of crosspoint networks have ES=*** is calculated for banyan,Clos,parallel packet,fully interconnected and recirculation switching *** ES value for the banyan switching network is larger than that for other networks,and switching networks are classified into three types that have absolute/linear/denied spatial extensibility according to the limES *** is demonstrated that a switching network has the largest ES value when it contains only the five types of switching unit for which ES=***,a group-switching-first self-routing banyan switching network with lower blocking probability and time delay is deduced,and the ES method is contrasted with two other methods of evaluating spatial extensibility in terms of their mathematical expressions and intuitive graphics,for the five types of switching network listed above.

关键词： spatial extensibility connection state efciency of switching banyan fully interconnected recirculation switching network

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Topic Representation:A Novel Method of Tag Recommendation for Text 2

Topic Representation:A Novel Method of Tag Recommendation fo...

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Shangru Zhong Kai Lei Xiaohui Huang Jincheng Wu Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology & Applications School of Electronic and Computer Engineering Peking University

ISBN: (纸本)9781509036202

With tags widely used in organizing and searching contents in massive data era,how to automatically generate appropriate tags of resource for users became a hot issue on social networks *** recommendation for text resource can be modeled as a keyword extraction problem,hence topic modeling such as LDA which extracts latent semantic topics from text is suitable for tag ***,latent topics are too coarse-grained to describe ***,LDA trains corpus globally without considering context ***,topics generated are difficult to be quantifiably *** problems lead to the poor quality of tag recommendation in topic model based *** this paper,we propose topic representation method,which introduces embedding semantic representation into topic *** results of evaluation on real social networks show that the proposed method improves the quality of tag recommendation for Chinese text resource,when comparing with traditional LDA-based method,which demonstrates the effectiveness of modifying topic modeling.

关键词： Tag recommendation topic model LDA word embedding

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Syndromes Diagnostic Model for Coronary Artery Disease(CAD):An Improved Na?ve Bayesian Classification Model Based on Attribute Relevancy 2

Syndromes Diagnostic Model for Coronary Artery Disease(CAD):...

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Kai Lei Lizhu Zhang Ying Shen Xiaohui Huang Jincheng Wu Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology and Applications School of Electronic and Computer Engineering Peking University

ISBN: (纸本)9781509036202

Clinical decision support is a probabilistic and quantitative method learning used for problem modeling in situations of ***,mathematical methods such as probability learning and statistical analysis is increasingly used in the medical field to assure the accuracy of the diagnoses and optimize the *** study describes the design and implementation of an improved weighted Na?ve Bayes classifier based on the idea that there is a certain level of dependency between *** the coronary artery disease,clinical data and patients' medical records were used to train the improved Na?ve Bayes classifier to compute the possible disease manifestations,the priori probability and the posterior probability and to carry out the disease ***,we were able to improve the precision of the improved weighted Na?ve Bayes classifier to 79%(increase of 12% compared to Na?ve Bayes).The suitability of the improved Na?ve Bayes classifiers for carrying out diagnosis allows them to be utilized to distinguish syndromes and signs based on unobserved evidence.

关键词： Na?ve Bayes decision support diagnostic classification attribute relevancy negative symptoms coronary artery disease

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