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Density based Collective Spatial keyword Query

Density based Collective Spatial Keyword Query

第8届语义知识与网络国际会议(2012 Eighth International Conference on Semanties,Knowledge and Grids )(SKG2012)

作者： Li Zhang Xiaoping Sun Hai Zhuge Knowledge Grid Lab Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

Geographic objects with descriptive text are gaining in prevalence in many web services such as Google *** keyword query which combines both the location information and textual description stands out in recent *** works mainly focus on finding top-k Nearest Neighbours where each node has to match the whole querying keywords.A collective query has been proposed to retrieve a group of objects nearest to the query object such that the group's keywords cover query's keywords and has the shortest inner-object *** the previous method does not consider the density of data objects in the spatial *** practice,a group of dense data objects around a query point will be more interesting than those sparse data *** distance of data objects of a group cannot reflect the density of the *** overcome this shortage,we proposed an approximate algorithm to process the collective spatial keyword query based on density and inner *** empirical study shows that our algorithm can effectively retrieve the data objects in dense areas.

关键词： Indexes Spatial databases Approximation algorithms Clustering algorithms Communities Query processing Semantics

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Iterative Annotation Transformation with Predict-Self Reestimation for Chinese Word Segmentation 12

Iterative Annotation Transformation with Predict-Self Reesti...

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Conference on Empirical Methods in Natural Language processing

作者： Wenbin Jiang Fandong Meng Qun Liu Yajuan Lue Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781622765034

In this paper we first describe the technology of automatic annotation transformation, which is based on the annotation adaptation algorithm (Jiang et al., 2009). It can automatically transform a human-annotated corpus from one annotation guideline to another. We then propose two optimization strategies, iterative training and predict-self reestimation, to further improve the accuracy of annotation guideline transformation. Experiments on Chinese word segmentation show that, the iterative training strategy together with predict-self reestimation brings significant improvement over the simple annotation transformation baseline, and leads to classifiers with significantly higher accuracy and several times faster processing than annotation adaptation does. On the Penn Chinese Treebank 5.0, it achieves an F-measure of 98.43%, significantly outperforms previous works although using a single classifier with only local features.

关键词： ANNOTATIONS Classifiers Iterative Word Chinese Chinese philosophy Chinese art AUTOMATIC

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Faceted Navigation on Text

Faceted Navigation on Text

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第8届语义知识与网络国际会议(2012 Eighth International Conference on Semanties,Knowledge and Grids )(SKG2012)

作者： Bei Xu Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences Beijing 100080 China

This paper proposes a method which is not for summarization but for extracting multiple facets from a text according to the keyword sets representing readers’ interests,so that readers can obtain the interested facets and carry out faceted navigation on text.A facet is a meaningful combination of the subsets of the *** text process technologies are mostly based on text features such as word frequency,sentence location,syntax analysis and discourse *** approaches neglect the cognition process of human *** proposed method considers human reading *** show that the facet extraction is effective and robust.

关键词： Semantics Humans Robustness Navigation Feature extraction Drugs information processing

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Left-to-right tree-to-string decoding with prediction

Left-to-right tree-to-string decoding with prediction

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2012 Joint Conference on Empirical Methods in Natural Language processing and Computational Natural Language Learning, EMNLP-CoNLL 2012

作者： Feng, Yang Liu, Yang Liu, Qun Cohn, Trevor Department of Computer Science University of Sheffield Sheffield United Kingdom State Key Laboratory on Intelligent Technology and Systems Department of Computer Sci. and Tech. Tsinghua University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781937284435

Decoding algorithms for syntax based machine translation suffer from high computational complexity, a consequence of intersecting a language model with a context free grammar. Left-to-right decoding, which generates the target string in order, can improve decoding efficiency by simplifying the language model evaluation. This paper presents a novel left to right decoding algorithm for tree-to-string translation, using a bottom-up parsing strategy and dynamic future cost estimation for each partial translation. Our method outperforms previously published tree-to-string decoders, including a competing left-to-right method. © 2012 Association for Computational Linguistics.

关键词： Context free grammars

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CloudLCA: finding the lowest common ancestor in metagenome analysis using cloud computing

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Protein & Cell 2012年第2期3卷 148-152页

作者： Guoguang Zhao Dechao Bu Changning Liu Jing Li Jian Yang Zhiyong Liu Yi Zhao Runsheng Chen Bioinformatics Research Group Key Laboratory of Intelligent Information ProcessingAdvanced Computing Research LaboratoryInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Bioinformatics Laboratory and National Laboratory of Biomacromolecules Institute of BiophysicsChinese Academy of SciencesBeijing 100101China State Key Laboratory for Molecular Virology and Genetic Engineering National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 100176China Graduate School of the Chinese Academy of Sciences Beijing 100190China

Estimating taxonomic content constitutes a key problem in metagenomic sequencing data ***,extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently availab.e ***,we present CloudLCA,a parallel LCA algorithm that significantly improves the efficiency of determining taxonomic composition in metagenomic data *** show that CloudLCA(1)has a running time nearly linear with the increase of dataset magnitude,(2)displays linear speedup as the number of processors grows,especially for large datasets,and(3)reaches a speed of nearly 215 million reads each minute on a cluster with ten thin *** comparison with MEGAN,a well-known metagenome analyzer,the speed of CloudLCA is up to 5 more times faster,and its peak memory usage is approximately 18.5%that of MEGAN,running on a fat *** can be run on one multiprocessor node or a *** is expected to be part of MEGAN to accelerate analyzing reads,with the same output generated as MEGAN,which can be import into MEGAN in a direct way to finish the following ***,CloudLCA is a universal solution for finding the lowest common ancestor,and it can be applied in other fields requiring an LCA algorithm.

关键词： CloudLCA metagenome analysis cloud computing

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Irregular community discovery for cloud service improvement

Irregular community discovery for cloud service improvement

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作者： Liu, Jin Zhou, Jing Wang, Junfeng Zhang, Feng Liu, Fei State Key Lab. of Software Engineering Wuhan University Wuhan 430072 China Lab. of Complex Systems and Intelligence Science Institute of Automation CAS Beijing 100190 China State Key Lab. for Novel Software Technology Nanjing University Nanjing China School of Computer Science Communication University of China Beijing 100024 China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100190 China College of Computer Science Sichuan University Chengdu 610064 China Software School Sun Yat-sen University Guangzhou 510006 China

Utility services provided by cloud computing rely on virtual customer communities forming spontaneously and evolving continuously. Clarifying the explicit boundaries of these communities is thus essential to the quality of utility services in cloud computing. Communities with overlapping features or prominent peripheral vertexes are usually typical irregular communities. Traditional community identification algorithms are limited in discovering irregular topological structures from CR networks, whereas these irregular shapes typically play an important role in finding prominent customers which are ignored in social CRM otherwise. We present a novel method of discovering irregular communities. It firstly finds and merges primitive maximal cliques and the irregular features of overlapping and prominent sparse vertices are further considered. An empirical case and a methodology comparison confirm the feasibility and efficiency of our approach. © 2012 Springer Science+Business Media, LLC.

关键词： Cloud computing

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TINAC: A fast and effective web video topic detection framework

TINAC: A fast and effective web video topic detection framew...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Xiang Ao Fuzhen Zhuang Qing He Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

Most of the previous works for web video topic detection(e.g., graph-based co-clustering method) always encounter the problem of real-time topic detection, since they all suffer from the high computation complexity. Therefore, a fast topic detection is needed to meet users' or administrators' requirement in real-world scenarios. Along this line, we propose a fast and effective topic detection framework, in which video streams are first partitioned into buckets using a time-window function, and then an incremental hierarchical clustering algorithm is developed, finally a video-based fusion strategy is used to integrate information from multiple modalities. Furthermore, a series of novel similarity metrics are defined in the framework. The experimental results on three months' YouTube videos demonstrate the effectiveness and efficiency of the proposed method.

关键词： Clustering algorithms Measurement Partitioning algorithms Streaming media Vectors Visualization Complexity theory

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Nested granular local learning

Nested granular local learning

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IEEE International Conference on Granular computing (GRC)

作者： Hong Hu Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

Local learning approaches are especially easy for parallel processing, so they are very important for cloud computing. In 1997, Lotti A. Zadeh proposed the concept of Granular computing (GrC). Zadeh proposed that there are three basic concepts that underlie human cognition: granulation, organization and causation and a granule being a clump of points (objects) drawn together by indistinguishability, similarity, proximity or functionality. In this paper, we give out a novel local learning approach based on the concept of Granular computing named as "nested local learning NGLL". The experiment shows that the novel NGLL approach is better than the probabilistic latent semantic analysis (PLSA).

关键词： Probabilistic logic Pattern recognition

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Mining association rules on qing court medical records: Semantic abstraction and standardization

Mining association rules on qing court medical records: Sema...

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2012 IEEE 12th International Conference on Computer and information technology, CIT 2012

作者： Cao, Cong Wang, Weimin Cao, Cungen Zang, Liangjun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9780769548586

To explore the association relations among disease, pathogenesis, physician, symptoms and drug, we adapt a variational Apriori algorithm for discovering association rules on a dataset of the Qing Court Medical Records. There are five types of semantic associations we intend to discover, including Disease-Pathogenesis-Drug set(DPaD), Disease-Symptoms-Drug set (DSyD), Disease-Drug set (DD), Disease-Physician-Drug set (DPhD) and Disease-Drug Category Set (DDC). To solve the synonymity problem and the data sparseness problem, we give a mapping strategy which maps pathogenesis to standardized forms and maps drugs to drug categories. With the mapping strategy the number of frequent drug sets rises from 287 to 1184. The experimental results indicate that our method with the mapping strategy is an effective way to acquire valuable semantic association rules. © 2012 IEEE.

关键词： Association rules

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ICT: System Description for CoNLL-2012 12

ICT: System Description for CoNLL-2012

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

作者： Hao Xiong Qun Liu Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

ISBN: (纸本)9781627484046

In this paper, we present our system description for the CoNLL-2012 coreference resolution task on English, Chinese and Arabic. We investigate a projection-based model in which we first translate Chinese and Arabic into English, run a publicly availab.e coreference system, and then use a new projection algorithm to map the coreferring entities back from English into mention candidates detected in the Chinese and Arabic source. We compare to a baseline that just runs the English coreference system on the supplied parses for Chinese and Arabic. Because our method does not beat the baseline system on the development set, we submit outputs generated by the baseline system as our final submission.

关键词： Arabic language English English language Projection algorithms Chinese Chinese philosophy Chinese art BASELINE information technology information and communication technology International Criminal Tribunal Coreference Resolution

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