期刊：
International Conference on Information and Knowledge Management, Proceedings,2013年:1237-1240
作者机构：
[Luo, Jing; Tu, Xinhui; Liu, Maofu] College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China;[Li, Bo; He, Tingting] Department of Computer Science, Central China Normal University, Wuhan, China;[Luo, Jing; Tu, Xinhui; Liu, Maofu] Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, China
会议名称：
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
摘要：
A main challenge in applying translation language models to information retrieval is how to estimate the "true" probability that a query could be generated as a translation of a document. The state-of-art methods rely on document-based word co-occurrences to estimate word-word translation probabilities. However, these methods do not take into account the proximity of co-occurrences. Intuitively, the proximity of co-occurrences can be exploited to estimate more accurate translation probabilities, since two words occur closer are more likely to be related. In this paper, we study how to explicitly incorporate proximity information into the existing translation language model, and propose a proximity-based translation language model, called TM-P, with three variants. In our TM-P models, a new concept (proximity-based word co-occurrence frequency) is introduced to model the proximity of word co-occurrences, which is then used to estimate translation probabilities. Experimental results on standard TREC collections show that our TM-P models achieve significant improvements over the state-of-the-art translation models. Copyright 2013 ACM.
期刊：
ACM International Conference Proceeding Series,2012年:1263-1272
通讯作者：
Chen, X.(bruce.chen@drexel.edu)
作者机构：
[Chen, Xin; Hu, Xiaohua; An, Yuan] College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, United States;[Zhou, Zhongna] Dept. of ECE, University of Missouri, Columbia, MO, United States;[He, Tingting] Dept. of Computer Science, Central China Normal University, Wuhan, China;[Park, E.K.] California State University - Chico, Chico, CA 95929, United States
通讯机构：
College of Information Science and Technology, Drexel University, United States
会议名称：
21st ACM International Conference on Information and Knowledge Management, CIKM 2012
期刊：
ACM International Conference Proceeding Series,2012年:2179-2183
通讯作者：
Hu, X.(xiaohu.tony.hu@gmail.com)
作者机构：
[Wang, Jianwen; Tu, Xinhui; He, Tingting] Department of Computer Science, Central China Normal University, Wuhan, China;College of Information Science and Technology, Drexel University, Philadelphia, PA, United States;[Hu, Xiaohua] Department of Computer Science, Central China Normal University, Wuhan, China<&wdkj&>College of Information Science and Technology, Drexel University, Philadelphia, PA, United States
通讯机构：
Department of Computer Science, Central China Normal University, China
会议名称：
21st ACM International Conference on Information and Knowledge Management, CIKM 2012
会议时间：
October 29, 2012 - November 2, 2012
会议地点：
Maui, HI, United states
关键词：
academic network search;gibbs sampling;topic model
期刊：
Communications in Computer and Information Science,2011年135(PART 2):372-377 ISSN：1865-0929
通讯作者：
He, Tingting
作者机构：
[Chen, Jinguang; He, Tingting] Huazhong Normal Univ, Engn & Res Ctr Informat Technol Educ, Wuhan 430079, Peoples R China.
通讯机构：
[He, Tingting] H;Huazhong Normal Univ, Engn & Res Ctr Informat Technol Educ, Wuhan 430079, Peoples R China.
会议名称：
International Conference on Intelligent Computing and Information Science
会议时间：
JAN 08-09, 2011
会议地点：
Chongqing, PEOPLES R CHINA
会议主办单位：
[Chen, Jinguang;He, Tingting] Huazhong Normal Univ, Engn & Res Ctr Informat Technol Educ, Wuhan 430079, Peoples R China.
会议论文集名称：
Communications in Computer and Information Science
关键词：
Historical information removal;Novelty detecting;Query-focused Update Summarization;Update Summarization
摘要：
Current researches of query-focused update summarization often overlook influence of the query upon historical information. In this paper, we proposed a new information detection method which treats query-relevant and query-irrelevant old information in different way. Old information in the document set is removed in the process of sentence scoring stage instead of in additional stage after the sentences scored. Experiment results on TAC 2009 shows effectiveness of our method. KeywordsUpdate Summarization-Historical information removal-Novelty detecting-Query-focused Update Summarization
