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检索条件"机构=Laboratory of Intelligent Information ProcessingInstitute of Computing Technology"

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

Iterative Annotation Transformation with Predict-Self Reesti...

引用

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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A MMSM-based Hybrid Method for Chinese MicroBlog Word Segmentation 2

A MMSM-based Hybrid Method for Chinese MicroBlog Word Segmen...

引用

2nd CIPS-SIGHAN Joint Conference on Chinese Language Processing, CLP 2012

作者： Sun, Xiao Li, Chengcheng Tang, Chenyi Ye, Jiaqi AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Anhui Hefei230009 China School of Computer Science and Engineering Dalian University of Technology Liaoning Dalian116023 China

After years of researches, Chinese word segmentation has achieved quite high precisions for formal style text. However, the performance of segmentation is not so satisfying for MicroBlog corpora. In this paper we describe a scheme for Chinese word segmentation for, MicroBlog which integrates the character-based and word-based information in the directed graph generated by MMSM model. Word-level information is effective for analysis of known words, while character-level information is useful for analysis of unknown words. A multi-chain unequal states CRF model is proposed. The proposed multi-chain unequal states CRF has two state chains with unequal states which can recognize the POS tag simultaneously. The hybrid model was effective and adopted in real-world system. © 2021 CLP 2012 - 2nd CIPS-SIGHAN Joint Conference on Chinese Language Processing. All Rights Reserved.

关键词： Directed graphs

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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...

引用

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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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

引用

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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Memory performance prediction of web server applications based on grey system theory

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14th Asia Pacific Web technology Conference, APWeb 2012

作者： Huang, Faliang Zhang, Shichao Yuan, Changan Zhong, Zhi Faculty of Software Fujian Normal University Fuzhou 350007 China Faculty of Engineering and Information Technology University of Technology Broadway P.O. Box 123 Sydney NSW 2007 Australia Science Computing and Intelligent Information Processing GuangXi Higher Education Key Laboratory Nanning 530023 China

ISBN: (纸本)9783642292521

With the success of internet, recently more and more companies start to run web-based business. While running e-business sites, many companies have encountered unexpected degeneration of their web server applications performance, which may lead to loss of customers. Many managers wish to have a decision-support tool that cancan answer such questions, such as "will my web server applications performance degenerate?", and "what are the main reasons of the degenerations?". In this paper we first propose a new memory performance prediction model of web server applications based on grey system theory. And then, a software system "Memory Performance Manager" (MPM) is developed for predicting memory performance of the web server applications. Massive experiments demonstrate that the effectiveness of MPM's in predicting web server memory performances. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Managers

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Multi-task Semi-supervised Semantic Feature Learning for Classification

Multi-task Semi-supervised Semantic Feature Learning for Cla...

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IEEE International Conference on Data Mining (ICDM)

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

Multi-task learning has proven to be useful to boost the learning of multiple related but different tasks. Meanwhile, latent semantic models such as LSA and LDA are popular and effective methods to extract discriminative semantic features of high dimensional dyadic data. In this paper, we present a method to combine these two techniques together by introducing a new matrix tri-factorization based formulation for semi-supervised latent semantic learning, which can incorporate labeled information into traditional unsupervised learning of latent semantics. Our inspiration for multi-task semantic feature learning comes from two facts, i.e., 1) multiple tasks generally share a set of common latent semantics, and 2) a semantic usually has a stable indication of categories no matter which task it is from. Thus to make multiple tasks learn from each other we wish to share the associations between categories and those common semantics among tasks. Along this line, we propose a novel joint Nonnegative matrix tri-factorization framework with the aforesaid associations shared among tasks in the form of a semantic-category relation matrix. Our new formulation for multi-task learning can simultaneously learn (1) discriminative semantic features of each task, (2) predictive structure and categories of unlabeled data in each task, (3) common semantics shared among tasks and specific semantics exclusive to each task. We give alternating iterative algorithm to optimize our objective and theoretically show its convergence. Finally extensive experiments on text data along with the comparison with various baselines and three state-of-the-art multi-task learning algorithms demonstrate the effectiveness of our method.

关键词： Semantics Optimization Feature extraction Data mining Data models Joints Iterative methods

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Leaf image recognition using Fourier transform based on ordered sequence

Leaf image recognition using Fourier transform based on orde...

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8th International Conference on intelligent computing technology, ICIC 2012

作者： Yang, Li-Wei Wang, Xiao-Feng Department of Automation University of Science and Technology of China Hefei Anhui 230027 China Intelligent Computing Laboratory Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O. Box 1130 Hefei Anhui 230031 China Key Lab. of Network and Intelligent Information Processing Hefei University Hefei Anhui 230601 China

ISBN: (纸本)9783642315879

There are a number of leaf recognition methods, but most of them are based on Euclidean space. In this paper, we will introduce a new description of feature for the leaf image recognition, which represents the leaf contour with the ordered sequence. For a leaf image, points on the contour represent the most important information of the leaf. Thus, by extracting serial points of the leaf contour, the unique corresponding ordered sequence can be obtained for a contour. Then, we can compute the amplitude-frequency feature by performing the Discrete Fourier transform on the ordered sequence. Since the low-frequency part of the Fourier transform represents the global information and the high-frequency part the local details, we can adopt the amplitude-frequency feature for leaf image recognition. Experimental results on the famous Swedish library and ICL library show that the proposed feature is effective for leaf image recognition. © 2012 Springer-Verlag.

关键词： Image recognition

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A secure message delivery scheme with path tracking for delay tolerant networks

A secure message delivery scheme with path tracking for dela...

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2012 1st IEEE International Conference on Communications in China, ICCC 2012

作者： Jia, Zhongtian Raffay, Muhammad Ali Li, Lixiang Lin, Xiaodong Yang, Yixian Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan 250022 China Information Security Center Beijing University of Posts and Telecommunications Beijing 100876 China Faculty of Business and Information Technology University of Ontario Institute of Technology Oshawa ON L1H 7K4 Canada

ISBN: (纸本)9781467328159

In delay tolerant networks (DTNs), message delivery is operated in an opportunistic way through store-carry and forward relaying, and every DTN node is in anticipation of cooperation for data forwarding from others. Unfortunately, there always exist some selfish nodes that are reluctant to contribute to this cooperative data forwarding procedure so as to save their valuable storage buffer, limited computation power and precious energy. In order to stimulate nodes' willingness to participate in data forwarding, a number of incentive schemes have been proposed recently. However, most existing incentive schemes simply ignore efforts of nodes involved in message delivery if messages delivered fail to reach their destinations. Due to the nature of DTN, such as intermittent connectivity, it is not unusual to have unreliable message delivery, which results in unrewarded or wasted efforts for participating nodes and may discourage them from participating in future data forwarding. Therefore, it is crucial to recognize contribution of every node involved in a data forwarding procedure even the message it helps to forward doesn't successfully reach its destination. However, how to track all delivery paths so as to give every intermediate node some incentive for their cooperative efforts of data forwarding is still an open research problem. To address this problem, we propose a secure message forwarding scheme with path tracking. The proposed method is end-to-end secure with data source and identity authentication. In addition, it can thwart some well known attacks including edge inserting attack, sibling inserting attack and free riding attack. © 2012 IEEE.

关键词： Delay tolerant networks

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A novel online event analysis framework for micro-blog based on incremental topic modeling

A novel online event analysis framework for micro-blog based...

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13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing, SNPD 2012

作者： Ma, Huifang Wang, Bo Li, Ning Department of Computer Science Northwest Normal University Lanzhou Gansu 730070 China Department of Military Information Management Shanghai Branch PLA Nanjing Institute of Politics Shanghai 200433 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China

ISBN: (纸本)9780769547619

In this paper, we present a scalable implementation of a topic modeling (Adaptive Link-IPLSA) based method for online event analysis, which summarize the gist of massive amount of changing tweets and enable users to explore the temporal trends in topics. This model also can simultaneously maintain the continuity of the latent semantics to better capture the time line development of events. With the help of this model, users can quickly grasp major topics in these twitters. The preliminary results show that our method leads to more balanced and comprehensive improvement for online event detection compared to benchmark approaches. Additionally our algorithm is computationally feasible in near real-time scenarios making it an attractive alternative for capturing the rapidly changing dynamics of microblogs. © 2012 IEEE.

关键词： Semantics

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