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Learning lexicalized reordering models from reordering graphs

Learning lexicalized reordering models from reordering graph...

引用

48th Annual Meeting of the Association for Computational Linguistics, ACL 2010

作者： Su, Jinsong Liu, Yang Lü, Yajuan Mi, Haitao Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

ISBN: (纸本)9781617388088

Lexicalized reordering models play a crucial role in phrase-based translation systems. They are usually learned from the word-aligned bilingual corpus by examining the reordering relations of adjacent phrases. Instead of just checking whether there is one phrase adjacent to a given phrase, we argue that it is important to take the number of adjacent phrases into account for better estimations of reordering models. We propose to use a structure named reordering graph, which represents all phrase segmentations of a sentence pair, to learn lexicalized reordering models efficiently. Experimental results on the NIST Chinese-English test sets show that our approach significantly outperforms the baseline method. © 2010 Association for Computational Linguistics.

关键词： Translation (languages)

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Research on spectral clustering algorithms and prospects

Research on spectral clustering algorithms and prospects

引用

2010 2nd International Conference on Computer Engineering and technology, ICCET 2010

作者： Ding, Shifei Zhang, Liwen Zhang, Yu School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424463503

Along with the expansion and in-depth of the application domain of cluster analysis, one kind of new cluster algorithm called Spectral Clustering algorithm has been aroused great concern by scholars, Spectral Clustering algorithm is newly developing technique in the field of machine learning in recent years. Unlike the traditional clustering algorithms, this can solve the clustering of non-convex sphere of sample spaces and has globally optimal solution. This paper introduces the principle, the induction summary to the current research situation of Spectral Clustering algorithm as well as in various application domains. Firstly, the analysis and induction of some Spectral Clustering algorithms have been made from several aspects, such as the ideas of algorithm, key technology, advantage and disadvantage. On the other hand, some typical Spectral Clustering algorithms have been selected to analyze and compare. Finally, it points out the key problems and future directions. © 2010 IEEE.

关键词： Cluster analysis

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Assignment reduct and its calculation method in inconsistent incomplete decision system

Assignment reduct and its calculation method in inconsistent...

引用

IEEE International Conference on Granular computing (GRC)

作者： Zuqiang, Meng Zhongzhi, Shi Hua, Qin College of Computer Electronics and Information Guangxi University Nanning 530004 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9780769541617

Attribute reduction is a basic issue in rough set theory. But related researches on reduction in inconsistent incomplete decision system (IIDS) are currently rarely reported. In this paper, the concept of assignment reduct is extended from inconsistent complete decision system (ICDS) to IIDS and then the property of assignment-core attribute is analysed, and finally a method to calculate assignment reduct is put forward by using the heritable trait of assignment reduct. Example analysis shows that the proposed method is effective. This work provides an effective solution for attribute reduction in IIDS. © 2010 IEEE.

关键词： Rough set theory

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A parallel hyper-surface classifier for high dimensional data

A parallel hyper-surface classifier for high dimensional dat...

引用

2010 3rd International Symposium on Knowledge Acquisition and Modeling, KAM 2010

作者： He, Qing Wang, Qun Du, Chang-Ying Ma, Xu-Dong Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781424480050

The enlarging volumes of data resources produced in real world makes classification of very large scale data a challenging task. Therefore, parallel process of very large high dimensional data is very important. Hyper-Surface Classification (HSC) is approved to be an effective and efficient classification algorithm to handle two and three dimensional data. Though HSC can be extended to deal with high dimensional data with dimension reduction or ensemble techniques, it is not trivial to tackle high dimensional data directly. Inspired by the decision tree idea, an improvement of HSC is proposed to deal with high dimensional data directly in this work. Furthermore, we parallelize the improved HSC algorithm (PHSC) to handle large scale high dimensional data based on MapReduce framework, which is a current and powerful parallel programming technique used in many fields. Experimental results show that the parallel improved HSC algorithm not only can directly deal with high dimensional data, but also can handle large scale data set. Furthermore, the evaluation criterions of scaleup, speedup and sizeup validate its efficiency. © 2010 IEEE.

关键词： MapReduce

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Research of neural network algorithm based on FA and RBF

Research of neural network algorithm based on FA and RBF

引用

2010 2nd International Conference on Computer Engineering and technology, ICCET 2010

作者： Ding, Shifei Jia, Weikuan Su, Chunyang Chen, Jinrong School of Computer Science and Technology China University of Mining and Technology Xuzhou 221008 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100080 China

ISBN: (纸本)9781424463503

This paper proposes a radial basis function (RBF) neural network algorithm based on factor analysis (FA-RBF) with the architecture feature of RBF network when the data are high-dimensional and complex. By reducing the feature dimension of the original data, FA-RBF algorithm regards the data after dimension reduction as the inputs of the RBF network, and then trains and simulates the network. The algorithm obviously simplifies the network architecture. By analyzing an example, the results show when the algorithm's predicted precision is not reduced, the convergence velocity is improved, the running time is saved and the error of the predicted value is reduced. In order to test and verify the validity of the new algorithm, we compare it with the RBF neural network algorithm based on principal component analysis (PCA-RBF), the predicted results of FA-RBF algorithm are better than the results of RBF and PCA-RBF algorithm. © 2010 IEEE.

关键词： Radial basis function networks

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Nonparametric curve extraction based on ant colony system

Nonparametric curve extraction based on ant colony system

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作者： Tan, Qing He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Graduate University Chinese Academy of Sciences China

ISBN: (纸本)9781577354642

Curve extraction is an important and basic technique in image processing and computer vision. Due to the complexity of the images and the limitation of segmentation algorithms, there are always a large number of noisy pixels in the segmented binary images. In this paper, we present an approach based on ant colony system (ACS) to detect nonparametric curves from a binary image containing discontinuous curves and noisy points. Compared with the well-known Hough transform (HT) method, the ACS-based curve extraction approach can deal with both regular and irregular curves without knowing their shapes in advance. The proposed approach has many characteristics such as faster convergence, implicit parallelism and strong ability to deal with highly-noised images. Moreover, our approach can extract multiple curves from an image, which is impossible for the previous genetic algorithm based approach. Experimental results show that the proposed ACS-based approach is effective and efficient. Copyright © 2010, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Binary images

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引用

2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010

作者： Han, Xu Shi, Zhongzhi Niu, Wenjia Chen, Kunrong Yang, Xinghua Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China Graduate University of Chinese Academy of Sciences 100049 Beijing China

ISBN: (纸本)9780769541914

With the rapid development of XML language which has good flexibility and interoperability, more and more log files of software running information are represented in XML format, especially for Web services. Fault diagnosis by analyzing semi-structured and XML like log files is becoming an important issue in this area. For most related learning methods, there is a basic assumption that training data should be in identical structure, which does not hold in many situations in practice. In order to learn from training data in different structures, we propose a similarity-based Bayesian learning approach for fault diagnosis in this paper. Our method is to first estimate similarity degrees of structural elements from different log files. Then the basic structure of combined Bayesian network (CBN) is constructed, and the similarity-based learning algorithm is used to compute probabilities in CBN. Finally, test log data can be classified into possible fault categories based on the generated CBN. Experimental results show our approach outperforms other learning approaches on those training datasets which have different structures. © 2010 IEEE.

关键词： Bayesian networks

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Sorted label classifier chains for learning images with multi-label

Sorted label classifier chains for learning images with mult...

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18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10

作者： Liu, Xi Shi, Zhiping Li, Zhixin Wang, Xishun Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9781605589336

In the real world, images always have several visual objects instead of only one, which makes it difficult for conventional object recognition methods to deal with them. In this paper, we present a topologically sorted classifier chain method for learning images with multi-label. We first provide a means of generating a topo-logically sorted label chain ordering by employing a topological sort algorithm and then apply the chain ordering to the classifier chain model proposed by [1] to classify multi-label images. Our method can capture the correlations between labels very effectively due to the sorted label chain ordering and the advantages brought by classifier chain method. We evaluate the proposed method on Corel dataset and demonstrate the micro and macro F1 measures superior to the state-of-the-art methods. © 2010 ACM.

关键词： Object recognition

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Web service composition on the environment level

Web service composition on the environment level

引用

6th International Conference on Semantics, Knowledge and Grid, SKG 2010

作者： Cai, Guangjun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9780769541891

Service-oriented computing (SOC) promises a world of cooperating services loosely connected, creating dynamic business processes and agile applications. Service composition plays a very important role in it. One important problem related to service composition is about what the content of service description is. Most approaches focus on the description about web services themselves, and fail to consider the behaviour of the services in composition. This paper introduces a composition approach on the environment level. The basic idea of it is to develop a model-based approach by using the stability character of environment to encapsulate the changing of the service and the request. Our approach both facilitate to improve the composition efficiency and is more appropriate for compositing complex services or behaviour services based on its' behaviour. A running travel example is used to illustrate our approach. © 2010 IEEE.

关键词： Web services

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Voting conditional random fields for multi-label image classification

Voting conditional random fields for multi-label image class...

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2010 3rd International Congress on Image and Signal Processing, CISP 2010

作者： Wang, Xishun Liu, Xi Shi, Zhiping Shi, Zhongzhi Sui, Hongjian Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9781424465149

In our real world, there usually exist several different objects in one image, which brings intractable challenges to the traditional pattern recognition methods to classify the images. In this paper, we introduce a Conditional Random Fields (CRFs) model to deal with the Multi-label Image Classification problem. Considering the correlations of the objects, a second-order CRFs is constructed to capture the semantic associations between labels. Different initial feature weights are set to introduce the voting techniques for a better performance. We evaluate our methods on MSRC dataset and demonstrate high precision, recall and F1 measure, showing that our method is competitive. ©2010 IEEE.

关键词： Image classification

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