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A Chinese online document clustering algorithm based on hidden sentiment vector

Journal of computational Information Systems 2011年第6期7卷 1974-1981页

作者： Yang, Hui Wang, Zhe Zhou, Xu Wang, Zuo Zhou, Tong College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory for Symbol Computation and Knowledge Engineering Ministry of Education China

The core idea of clustering algorithm is the division of data into groups of similar objects. Some clustering algorithms are proven good performance on document clustering, such as k-means and UPGMA etc. However, few document clustering algorithms pay attention to the hidden sentiment which is a very important feature of the documents. This paper presents an improved k-means algorithm (HSK-Means) based on hidden sentiment vector for Chinese document clustering. Chinese dependency grammar rules are used to extract document feature and evaluate hidden sentiment. A new method for selecting initial cluster centroids based on rank mechanism of similarity is also proposed to improve accuracy of clustering algorithm. The experimental results on real online document sets illustrate that the performance of HSK-Means algorithm is better than classic document clustering algorithms. Copyright © 2011 Binary Information Press.

关键词： Information retrieval

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A kind of improving HMM model and using in the visual speech recognition

A kind of improving HMM model and using in the visual speech...

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2011 International Conference on Computer Science and Service System, CSSS 2011

作者： Zhang, Zeliang Li, Xiongfei Yang, Chengjia Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Beihua University Jilin China

ISBN: (纸本)9781424497638

Visual speech-lip reading, making the computer understands what do speakers want to express through observing the lip direction of them. The most simply method of lip reading in early stage is to compare between characters from the frozen pictures and templates being stored. It neglects the character is changing with time. This method is very simply, but it only can classify the simple elements not the words, so it couldn't render great serves to speech recognition. Afterwards the adoption of behavioral characteristics is becoming more widespread. Because of the superior of Hidden Markov model (HMM), it can be applied in speech recognition widely. In recent years, it is also used in the research of lip-reading recognition. The classical HMM model makes two hypotheses: hidden expropriation hypothesis: the state at t+1 is only conditioned by the state at t, not the state before;the expropriation hypothesis from hidden state to visible state: the visible state at t only conditioned by the hidden state at t, not the state before. Such kind of hypothesis is not very reasonable in some practical application (such as lip-reading). In some kind condition, the state at t is not only conditioned by t-1, but also t-2. Therefore this thesis revises the assumed condition classical HMM to derive a new HMM model and algorithm, and applying it into lip-reading recognition to increase the discrimination. © 2011 IEEE.

关键词： Hidden Markov models

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Support vector description of clusters for image annotation

Support vector description of clusters for image annotation

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International Workshop on Advanced computational Intelligence and Intelligent Informatics, IWACIII 2011

作者： Sun, Liang Yoshida, Shinichi Ge, Hongwei Liang, Yanchun College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China School of Information Kochi University of Technology Kochi 7828502 Japan Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian 116024 China

Image annotation is a challenging problem due to the rapid growing of real world image archives. In this paper, we propose a novel approach to the solving of this problem based on a variant of the support vector clustering (SVC) algorithm, i.e., the support vector description of clusters. The system has two major components, the training process and the annotating process. In the training process, clusters of image manually annotated by descriptive words are used as training instances. Each cluster is described by a one-cluster SVC model. The proposed model can exploit the advantage of SVC for its ability to delineate cluster boundaries of arbitrary shape. Moreover, the training process of the one-cluster SVC model is formulated as the process of building density estimator for underlying distribution of the cluster. In the annotating process, for a test image, the probability of this instance being generated by each model is computed. And then the relevant words are selected based on the obtained probabilities. Simulated experiments were conducted on the Corel60k data set. The results demonstrate the performance of the proposed algorithm, compared with the performance of other algorithms.

关键词： Image annotation

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Communities Detection with Applications to Real-World Networks Analysis

Communities Detection with Applications to Real-World Networ...

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International Conference on computational Intelligence and Security

作者： Bo Yang School of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China

Community structure is one of non-trivial topological properties ubiquitously demonstrated in real-world complex networks. Related theories and approaches are of fundamental importance for understanding the functions of networks. Previously, we have proposed a probabilistic algorithm called the NCMA to efficiently as well as effectively mine communities from real-world networks. Here, we show that the NCMA can be readily extended and applied to address a wide range of network oriented applications beyond community detection including ranking, characterizing and searching real world networks.

关键词： Communities Probability distribution Dolphins Social network services Probabilistic logic Prediction algorithms Clustering algorithms

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An Approach for Calculating Semantic Similarity between Words Using WordNet

An Approach for Calculating Semantic Similarity between Word...

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International Conference on Digital Manufacturing and Automation (ICDMA)

作者： Dou Hao Wanli Zuo Tao Peng Fengling He College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China

This paper presents a new edge-counting based method using Word Net to compute the similarity. The method achieves a similarity that perfectly fits with human rating and effectively simulate the human tHought process that is people prefer to consider more differences when the semantic distance between two word is closer, and vice versa. At last, we weigh up our model against a benchmark set by human similarity judgment, and obtain a much improved result compared with other methods.

关键词： Semantics Humans computational modeling Correlation Educational institutions Benchmark testing Smoothing methods

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A kind of improving HMM model and using in the visual speech recognition

A kind of improving HMM model and using in the visual speech...

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International Conference on Computer Science and Service System (CSSS)

作者： Zeliang Zhang Xiongfei Li Chengjia Yang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Beihang University Jilin China

Visual speech-lip reading, making the computer understands what do speakers want to express through observing the lip direction of them. The most simply method of lip reading in early stage is to compare between characters from the frozen pictures and templates being stored. It neglects the character is changing with time. This method is very simply, but it only can classify the simple elements not the words, so it couldn't render great serves to speech recognition. Afterwards the adoption of behavioral characteristics is becoming more widespread. Because of the superior of Hidden Markov model (HMM), it can be applied in speech recognition widely. In recent years, it is also used in the research of lip-reading recognition. The classical HMM model makes two hypotheses: hidden expropriation hypothesis: the state at t+1 is only conditioned by the state at t, not the state before; the expropriation hypothesis from hidden state to visible state: the visible state at t only conditioned by the hidden state at t, not the state before. Such kind of hypothesis is not very reasonable in some practical application (such as lip-reading). In some kind condition, the state at t is not only conditioned by t-1, but also t-2. Therefore this thesis revises the assumed condition classical HMM to derive a new HMM model and algorithm, and applying it into lip-reading recognition to increase the discrimination.

关键词： Hidden Markov models Viterbi algorithm Speech recognition knowledge engineering Laboratories Electronic mail Medical services

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A study on improved hidden Markov models and applications to speech recognition

A study on improved hidden Markov models and applications to...

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International Conference on Computer Science and Service System (CSSS)

作者： Zeliang Zhang Xiongfei Li Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Beihang University Jilin China

Visual voice lip-reading, so the computer can understand what the speakers want to express direction by looking at their lips. Lip reading is the easiest way to compare the early characters and templates from the frozen image is stored. It ignores the very nature and time changes. This method is very simple, but it's just simple elements can be classified, then it may not show significant speech recognition services. Behavior was characterized by more and more common. Because of the hidden Markov model is superior (HMM), which can be widely used in speech recognition. In recent years, is also used to lip reading identification. Classical HMM model, so that the two assumptions: hidden assumptions collected: in t+1 the state can only be in this country is not in the state before t; from the hidden visible state hypothesis: only by regulating the t hide the visible state, rather than the previous state. This hypothesis is not very useful in some applications (such as lip reading) is reasonable. Under certain conditions, in the t state not only limits the t-1, but also t-2. Therefore, this study modified the assumptions of the classical HMM to derive a new HMM model and algorithms, and applied to the lip-reading recognition is increasing discrimination.

关键词： Hidden Markov models Mouth Speech recognition Cost accounting Training Viterbi algorithm Algorithm design and analysis

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Robust and efficient annotation based on ontology evolution for deep web data

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Journal of Computers 2011年第10期6卷 2029-2036页

作者： Chen, Kerui Zuo, Wanli Zhang, Fan He, Fengling Chen, Yongheng Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China College of Information Engineering Zhongzhou University Zhengzhou 450044 China

Among those researches in Deep Web, compared to research of data extraction which is more mature, the research of data annotation is still at its preliminary stage. Currently, although the approach of applying ontology in data annotating has been approved by most researchers, there are many weaknesses existed, such as the complexity of the ontology, as well as the limitation on static ontology's ability to annotate new pages. Respond to those problems, this paper proposes a robust, highly efficient data annotation method based on ontology evolution. It needs to be noticed that this paper defines a simpler ontology which can improve annotating efficiency significantly. Experiments indicate that this method could improve the accuracy and efficiency of data annotation. © 2011 ACADEMY PUBLISHER.

关键词： Ontology

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Optimization strategy of top-down join enumeration on modern multi-core CPUs

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Journal of Computers 2011年第10期6卷 2004-2012页

作者： Zuo, Wanli Chen, Yongheng He, Fenglin Chen, Kerui Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China

Most contemporary database systems query optimizers exploit System-R's bottom-up dynamic programming method (DP) to find the optimal query execution plan (QEP) without evaluating redundant subplans. The distinguished exceptions are Volcano/Cascades using transforms to generate new plans according to a topdown approach. As recent research has revealed, bottom-up dynamic programming can improve performance with respect to the shape of the join graph and parallelism. However top-down join enumeration dynamic programming method can derive upper bounds for the costs of the plans it generates which is not available to typical bottom-up DP method. In this paper, we propose a comprehensive and practical framework for parallelizing top-down dynamic programming query optimization with complex non-inner join in the multi-core processor architecture, referred as PTDhyp. We have implemented such a search strategy and experimental results show that can improve optimization time effective compared to known existing algorithms. © 2011 ACADEMY PUBLISHER.

关键词： Dynamic programming

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Operon Prediction by Markov Clustering

Operon Prediction by Markov Clustering

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2011·国际基因组学大会

作者： Wei Du Zhongbo Cao Yan Wang Enrico Blanzieri Chen Zhang Yanchun Liang College of Mathematics Jilin University Changchun 130012 China Department of Information and Communication Technology University of Trento Povo 38050 Italy College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China

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