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8th Asia information Retrieval Societies Conference, AIRS 2012

作者： Li, Long Song, Dandan Liao, Lejian Beijing Eng. Research Center of High Volume Language Information Processing and Cloud Computing Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9783642353406

We propose a general hierarchical vertical classification framework, which can automatically discover the inherent hierarchical structure of relationships among verticals based on flat datasets, and then build a hierarchical classifier. We conducted a set of comparison experiments to verify the performance of it, such as with flat vs hierarchical structure of relationships, as well as among different feature selection and classification methods. Experimental results show that the hierarchical classifiers built on the basis of the proposed framework make big improvements over the flat classifiers when classifying unseen web pages. Among them, the Support Vector Machine using Odds Ratio to select discriminative features performs best. © Springer-Verlag 2012.

关键词： Classification (of information)

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Microblog topic detection based on LDA model and single-pass clustering 8th

Microblog topic detection based on LDA model and single-pass...

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8th International Conference on Rough Sets and Current Trends in computing, RSCTC 2012

作者： Huang, Bo Yang, Yan Mahmood, Amjad Wang, Hongjun School of Information Science & Technology Southwest Jiaotong University Chengdu610031 China Key Lab of Cloud Computing and Intelligent Technology ChengduSichuan Province610031 China

ISBN: (纸本)9783642321146

Microblogging is a recent social phenomenon of Web2.0 technology, having applications in many domains. It is another form of social media, recognized as Real-Time Web Publishing, which has won an impressive audience acceptance and surprisingly changed online expression and interaction for millions of *** is observed that clustering by topic can be very helpful for the quick retrieval of desired information. We propose a novel topic detection technique that permits to retrieve in real-time the most emergent topics expressed by the community. Traditional text mining techniques have no special considerations for short and sparse microblog data. Keeping in view these special characteristics of data, we adopt Single-pass Clustering technique by using Latent Dirichlet Allocation (LDA) Model in place of traditional VSM model, to extract the hidden microblog topics information. Experiments on actual dataset results showed that the proposed method decreased the probabilities of miss and false alarm, as well as reduced the normalized detection cost. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Data mining

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ICT: System description for CoNLL-2012

ICT: System description for CoNLL-2012

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2012 Joint Conference on Empirical Methods in Natural Language processing and Computational Natural Language Learning, EMNLP-CoNLL 2012 - Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes

作者： Xiong, Hao Liu, Qun Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing100190 China

ISBN: (纸本)9781937284459

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 available 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. © 2012 Association for Computational Linguistics.

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Semi-supervised clustering ensemble based on collaborative training

Semi-supervised clustering ensemble based on collaborative t...

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7th International Conference on Rough Sets and Knowledge Technology, RSKT 2012

作者： Zhang, Jinyuan Yang, Yan Wang, Hongjun Mahmood, Amjad Huang, Feifei School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 China Key Lab. of Cloud Computing and Intelligent Technology Chengdu Sichuan Province 610031 China

ISBN: (纸本)9783642318993

Recent researches on data clustering is increasingly focusing on combining multiple data partitions as a way to improve the robustness of clustering solutions. Most of them focused on crisp clustering combination. Semi-supervised clustering uses a small amount of labeled data to aid and bias the clustering of unlabeled data. However, in this paper, we offer a semi-supervised clustering ensemble model based on collaborative training (SCET) and an unsupervised clustering ensemble mode based on collaborative training (UCET). In the ensemble step of SCET, semi-supervised learning is introduced. While in UCET, the knowledge used in SCET is replaced by information extracted from the base-clusterings. Then tri-training is used as consensus of clustering ensemble. The experiments on datasets from UCI machine learning repository indicate that the model improves the accuracy of clustering. © 2012 Springer-Verlag.

关键词： Supervised learning

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ICT: A system combination for Chinese semantic dependency parsing 1

ICT: A system combination for Chinese semantic dependency pa...

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1st Joint Conference on Lexical and Computational Semantics, *SEM 2012

作者： Xiong, Hao Liu, Qun Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing100190 China

ISBN: (纸本)9781937284220

The goal of semantic dependency parsing is to build dependency structure and label semantic relation between a head and its modifier. To attain this goal, we concentrate on obtaining better dependency structure to predict better semantic relations, and propose a method to combine the results of three state-of-The-Art dependency parsers. Unfortunately, we made a mistake when we generate the final output that results in a lower score of 56.31%in term of labeled Attachment Score (LAS), reported by organizers. After giving golden testing set, we fix the bug and rerun the evaluation script, this time we obtain the score of 62.8% which is consistentwith the results on developing set. We will report detailed experimental results with correct program as a comparison standard for further research. © 2012 Association for Computational Linguistics.

关键词： Semantics

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ICT: A translation based method for cross-lingual textual entailment 1

ICT: A translation based method for cross-lingual textual en...

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1st Joint Conference on Lexical and Computational Semantics, *SEM 2012

作者： Meng, Fandong Xiong, Hao Liu, Qun Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing100190 China

ISBN: (纸本)9781937284220

In this paper, we present our system description in task of Cross-lingual Textual Entailment. The goal of this task is to detect entailment relations between two sentences written in different languages. To accomplish this goal, we first translate sentences written in foreign languages into English. Then, we use EDITS1, an open source package, to recognize entailment relations. Since EDITS only draws monodirectional relations while the task requires bidirectional prediction, thus we exchange the hypothesis and test to detect entailment in another direction. Experimental results show that our method achieves promising results but not perfect results compared to other participants. © 2012 Association for Computational Linguistics.

关键词： Translation (languages)

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Parallel implementation of ant-based clustering algorithm based on hadoop

Parallel implementation of ant-based clustering algorithm ba...

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3rd International Conference on Swarm Intelligence, ICSI 2012

作者： Yang, Yan Ni, Xianhua Wang, Hongjun Zhao, Yiteng School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 China Key Lab. of Cloud Computing and Intelligent Technology Sichuan Province Chengdu 610031 China

ISBN: (纸本)9783642309755

Hadoop is a distributed system infrastructure of cloud computing. Based on the characteristics of ant-based clustering algorithm, the paper implements the parallelization of this algorithm using MapReduce on Hadoop. The Map function calculates the average similarity of the object with its neighborhood objects. The Reduce function processes the objects with the Map outputs and updates related information of both ants and the objects to get ready for the next job. Results on the Hadoop clusters show that our method can significantly improve the computational efficiency with the premise of maintaining clustering accuracy. © 2012 Springer-Verlag.

关键词： MapReduce

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Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform

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Journal of Systems Engineering and Electronics 2012年第3期23卷 453-459页

作者： Dong Liang Pu Yan Ming Zhu Yizheng Fan Kui Wang Key Lab of Intelligent Computing ＆ Signal Processing Ministry of Education Anhui University Hefei 230039 E R. China School of Electronics and Information Engineering Anhui University Hefei 230039 P. R. China School of Mathematics and Computation Sciences Anhui University Hefei 230039 P. R. China

A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.

关键词： point pattern matching nonsubsampled contourlet transform scale-invariant feature transform spectral algorithm.

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Energy-efficient network routing with discrete cost functions

Energy-efficient network routing with discrete cost function...

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9th Annual Conference on Theory and Applications of Models of Computation, TAMC 2012

作者： Wang, Lin Fernández Anta, Antonio Zhang, Fa Hou, Chenying Liu, Zhiyong Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Institute IMDEA Networks Spain China State Key Lab. for Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Graduate University of Chinese Academy of Sciences China

ISBN: (纸本)9783642299513

Energy consumption is an important issue in the design and use of networks. In this paper, we explore energy savings in networks via a rate adaptation model. This model can be represented by a cost-minimization network routing problem with discrete cost functions. We formulate this problem as an integer program, which is proved to be NP-hard. Then a constant approximation algorithm is developed. In our proposed method, we first transform the program into a continuous-cost network routing problem, and then we approximate the optimal solution by a two-step rounding process. We show by analysis that, for uniform demands, our method provides a constant approximation for the uniform network routing problem with discrete costs. A bicriteria network routing problem is also developed so that a trade-off can be made between energy consumption and network delay. Analytical results for this latter model are also presented. © 2012 Springer-Verlag.

关键词： Network routing

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Semi-supervised hierarchical co-clustering

Semi-supervised hierarchical co-clustering

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7th International Conference on Rough Sets and Knowledge Technology, RSKT 2012

作者： Huang, Feifei Yang, Yan Li, Tao Zhang, Jinyuan Rutayisire, Tonny Mahmood, Amjad School of Information Science and Technology Provincial Key Lab. of Cloud Computing and Intelligent Technology Southwest Jiaotong University Chengdu 610031 China School of Computer Science Florida International University Miami FL 33199 United States

ISBN: (纸本)9783642318993

Hierarchical co-clustering aims at generating dendrograms for the rows and columns of the input data matrix. The limitation of using simple hierarchical co-clustering for document clustering is that it has a lot of feature terms and documents, and it also ignores the semantic relations between feature terms. In this paper a semi-supervised clustering algorithm is proposed for hierarchical co-clustering. In the first step feature terms are clustered using a little supervised information. In the second step, the feature terms are merged as new feature attributes. And in the last step, the documents and merged feature terms are clustered using hierarchical co-clustering algorithm. Semantic information is used to measure the similarity during the hierarchical co-clustering process. Experimental results show that the proposed algorithm is effective and efficient. © 2012 Springer-Verlag.

关键词： Clustering algorithms

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