作者机构：
College of Information Science and Technology, Drexel University, Philadelphia, PA, United States;Central China Normal University, Wuhan, Hubei, China
通讯机构：
College of Information Science and Technology, Drexel University, United States
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine
会议时间：
2012-10-04
会议地点：
Philadelphia, PA(US)
会议论文集名称：
2012 IEEE international conference on bioinformatics and biomedicine
摘要：
Using metagenomics to detect the global structure of microbial community remains a significant challenge. The structure of a microbial community and its functions are complicated not only because of the complex interactions among microbes but also their complicate interacting with confounding environmental factors. Recently dimension reduction methods such as Principle component analysis, Non-negative matrix factorization and Canonical correlation analysis have been employed extensively to investigate the complex structure embedded in metagenomic profiles which summarize the abundance of functional or taxonomic categorizations in metagenomic studies. However, metagenomic profiles are not necessary to meet the "Assumption of Linearity" behind these methods. Therefore it is worth to investigate how nonlinear methods can be utilized in metagenomic studies. In this paper, a nonlinear manifold learning method- Isomap is used to visualize and analyze large-scale metagenomic profiles. Isomap was applied on a large-scale Pfam profile which are derived from 45 metagenomes in Global Ocean Sampling expedition. In our result, a novel nonlinear structure of protein families is identified and the relationships among the identified nonlinear components and environmental factors of global ocean are explored. The results indicate the strength of nonlinear methods in learning the complex microbial structure. With the coming of the huge number of new sequenced metagenomes, nonlinear methods like Isomap could be necessary complementary tools to current widely used methods.
期刊：
ACM International Conference Proceeding Series,2012年:1622-1626
通讯作者：
Li, F.
作者机构：
Department of Computer Science, Central China Normal University, Wuhan, China;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China;College of Information Science and Technology, Drexel University, Philadelphia, PA, United States
通讯机构：
National Engineering Research Center for E-Learning, Central China Normal University, China
会议名称：
Proceedings of the 21st ACM international conference on Information and knowledge management
期刊：
NLP-KE 2011 - Proceedings of the 7th International Conference on Natural Language Processing and Knowledge Engineering,2011年:221-226
通讯作者：
Shen, H.
作者机构：
[Li, Fang] Engineering and Research Center for Information Technology on Education, Huazhong Normal University, Wuhan, China;[He, Tingting] Department of Computer Science, Huazhong Normal University, Wuhan, China;[Shen, Huiyu] Huazhong Normal University Press, Huazhong Normal University, Wuhan, China
通讯机构：
Huazhong Normal University Press, Huazhong Normal University, China
关键词：
Perplexity;Semantic Knowledge Acquisition;Tag;Topic Model
期刊：
International Conference on Information and Knowledge Management, Proceedings,2011年:1341-1346
通讯作者：
Chen, X.(bruce.chen@drexel.edu)
作者机构：
[Xiong, Zunyan; Chen, Xin; Hu, Xiaohua; An, Yuan] College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, United States;[Park, E.K.] California State University - Chico, Chico, CA 95929, United States;[He, Tingting] Dept. of Computer Science, Central China Normal University, Wuhan, China
摘要：
This paper presents a method which improves effectiveness of Naive Bayes text categorization by using cloud model. The traditional Naive Bayes text categorization directly uses term frequency to describe the relationship between words and categories. In deed, there are many words with high frequency do not have a close relevance with the category. To solve this problem, we introduce cloud model theory into Naive Bayes text classification and build a new feature selection system. By using numerical characteristics of cloud, we obtain more representative features. Experimental results on 20 Newsgroups show that our method can improve accuracy of text categorization remarkably.
期刊：
Proceedings of SPIE - The International Society for Optical Engineering,2011年8004:1-8 ISSN：0277-786X
通讯作者：
Liu Huayong
作者机构：
[He Tingting; Liu Huayong; Jiang Shanshan] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Liu Huayong] C;Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
会议名称：
第七届多光谱图象处理与模式识别国际学术会议
会议时间：
2011-11-01
会议地点：
桂林
会议主办单位：
[Liu Huayong;Jiang Shanshan;He Tingting] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
会议论文集名称：
第七届多光谱图象处理与模式识别国际学术会议论文集
关键词：
video summary;soccer video;user-oriented model;highlight extraction;multimodal analysis
摘要：
An advanced user-oriented summary extraction method for soccer video is proposed in this work. Firstly, an algorithm of user-oriented summary extraction for soccer video is introduced. A novel approach that integrates multimodal analysis, such as extraction and analysis of the stadium features, moving object features, audio features and text features is introduced. By these features the semantic of the soccer video and the highlight mode are obtained. Then we can find the highlight position and put them together by highlight degrees to obtain the video summary. The experimental results for sports video of world cup soccer games indicate that multimodal analysis is effective for soccer video browsing and retrieval.
期刊：
2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM 2011),2011年:3-9 ISSN：2156-1125
通讯作者：
Chen, Xin
作者机构：
[Chen, Xin; Hu, Xiaohua; An, Yuan] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.;[He, TingTing] Cent China Normal Univ, Dept Comp Sci, Wuhan, Hubei, Peoples R China.;[Wu, Xindong] Univ Vermont, Dept Comp Sci, Burlington, VT USA.
通讯机构：
[Chen, Xin] D;Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.
会议名称：
IEEE International Conference on Bioinformatics & Biomedicine (BIBM 2011)
会议时间：
2011-01-01
会议地点：
Atlanta, Georgia, USA
会议主办单位：
[Chen, Xin;Hu, Xiaohua;An, Yuan] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.^[He, TingTing] Cent China Normal Univ, Dept Comp Sci, Wuhan, Hubei, Peoples R China.^[Wu, Xindong] Univ Vermont, Dept Comp Sci, Burlington, VT USA.
会议论文集名称：
2011 IEEE International Conference on Bioinformatics and Biomedicine
关键词：
Bioinformatics databases;Biological data mining;Metagenomics;Probabilistic topic model
摘要：
In this paper, based on the functional elements derived from non-redundant CDs catalogue, we show that the configuration of functional groups in meta-genome samples can be inferred by probabilistic topic modeling. The probabilistic topic modeling is a Bayesian method that is able to extract useful topical information from unlabeled data. When used to study microbial samples (assuming that relative abundance of functional elements is already obtained by a homology-based approach), each sample can be considered as a 'document', which has a mixture of functional groups, while each functional group (also known as a 'latent topic') is a weight mixture of functional elements (including taxonomic levels, and indicators of gene orthologous groups and KEGG pathway mappings). The functional elements bear an analogy with 'words'. Estimating the probabilistic topic model can uncover the configuration of functional groups (the latent topic) in each sample. The experimental results demonstrate the effectiveness of our proposed method.
期刊：
Journal of Computational Information Systems,2011年7(13):4963-4971 ISSN：1553-9105
通讯作者：
He, T.(tthe@ccnu.edu.cn)
作者机构：
[Chen, Jinguang] Engineering and Research Center for Information Technology on Education, Huazhong Normal University, Wuhan 430079, China;[Chen, Jinguang] School of Teacher Education, Huzhou Teachers College, Huzhou 313000, China;[He, Tingting; Wan, Jian] Department of Computer Science and Technology, Huazhong Normal University, Wuhan 430079, China
通讯机构：
Department of Computer Science and Technology, Huazhong Normal University, China
摘要：
We present a novel graph ranking model to extract a diverse set of answers for complex questions via random walks over a negative-edge graph. We assign a negative sign to edge weights in an answer graph to model the redundancy relation among the answer nodes. Negative edges can be thought of as the propagation of negative endorsements or disapprovals which is used to penalize factual redundancy. As the ranking proceeds, the initial score of the answer node, given by its relevancy to the specific question, will be adjusted according to a long-term negative endorsement from other answer nodes. We empirically evaluate the effectiveness of our method by conducting a comprehensive experiment on two distinct complex question answering data sets.
期刊：
Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2010,2010年
通讯作者：
Yang, L.(yangliu721@gmail.com)
作者机构：
[He, Tingting; Yang, Liu] Department of Computer Science and Technology, Huazhong Normal University, Wuhan, Hubei, China;[Chen, Jinguang; Tu, Xinhui] Engineering and Research Center for Information Technology on Education, Huazhong Normal University, Wuhan, Hubei, China
通讯机构：
Department of Computer Science and Technology, Huazhong Normal University, China
期刊：
International Conference on Information and Knowledge Management, Proceedings,2010年:899-908
通讯作者：
Chen, X.(bruce.chen@drexel.edu)
作者机构：
[Lu, Caimei; Chen, Xin; Hu, Xiaohua] College of Information Science and Technology, Drexel University, Philadelphia, PA, United States;[Park, E.K.] CSI-CUNY, Staten Island, NY, United States;[He, Tingting] Dept. of Computer Science, Central China Normal University, Wuhan, China;[Rosen, Gail] Dept. of ECE, Drexel University, Philadelphia, PA, United States;[Zhou, Zhongna] Dept. of ECE, University of Missouri, Columbia, MO, United States
期刊：
Lecture Notes in Computer Science,2010年5993:370-381 ISSN：0302-9743
通讯作者：
Tu, Xinhui
作者机构：
[He, Tingting; Tu, Xinhui; Zhang, Maoyuan] Huazhong Normal Univ, Engn & Res Ctr Informat Technol Educ, Wuhan, Peoples R China.;[Chen, Long] Univ London Birkbeck Coll, London WC1E 7HU, England.;[Tu, Xinhui; Luo, Jing] Wuhan Univ Sci & Technol, Dept Comp Sci & Technol, Wuhan, Peoples R China.
通讯机构：
[Tu, Xinhui] H;Huazhong Normal Univ, Engn & Res Ctr Informat Technol Educ, Wuhan, Peoples R China.
会议名称：
32nd European Conference on Information Retrieval Research
会议时间：
MAR 28-31, 2010
会议地点：
Milton Keynes, ENGLAND
会议主办单位：
[Tu, Xinhui;He, Tingting;Zhang, Maoyuan] Huazhong Normal Univ, Engn & Res Ctr Informat Technol Educ, Wuhan, Peoples R China.^[Chen, Long] Univ London Birkbeck Coll, London WC1E 7HU, England.^[Tu, Xinhui;Luo, Jing] Wuhan Univ Sci & Technol, Dept Comp Sci & Technol, Wuhan, Peoples R China.
会议论文集名称：
Lecture Notes in Computer Science
关键词：
Information Retrieval;Language Model;Wikipedia
摘要：
Semantic smoothing for the language modeling approach to information retrieval is significant and effective to improve retrieval performance. In previous methods such as the translation model, individual terms or phrases are used to do semantic mapping. These models are not very efficient when faced with ambiguous words and phrases because they are unable to incorporate contextual information. To overcome this limitation, we propose a novel Wikipedia-based semantic smoothing method that decomposes a document into a set of weighted Wikipedia concepts and then maps those unambiguous Wikipedia concepts into query terms. The mapping probabilities from each Wikipedia concept to individual terms are estimated through the EM algorithm. Document models based on Wikipedia concept mapping are then derived. The new smoothing method is evaluated on the TREC Ad Hoc Track (Disks 1, 2, and 3) collections. Experiments show significant improvements over the two-stage language model, as well as the language model with translation-based semantic smoothing.
期刊：
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2010年:683-691
通讯作者：
Lu, C.(caimei.lu@drexel.edu)
作者机构：
[Park, Jung-Ran; Chen, Xin; Lu, Caimei; Hu, Xiaohua] College of Information Science and Technology, Drexel University, Philadelphia, PA, United States;[Li, Zhoujun] School of Computer Science and Engineering, Beihang University, Beijing, China;[He, Ting Ting] Department of Computer Science, Central China Normal University, Wuhan, China
通讯机构：
College of Information Science and Technology, Drexel University, United States
