http://www.bibsonomy.org/burst/user/renew/topicBibSonomy BuRST Feed for /user/renew/topic2008-07-26T21:29:50+02:00The big onehttp://www.bibsonomy.org/bibtex/25f0e2f5fef43b240f92eaa24fd203cc0/renewrenew2008-04-21T13:44:43+02:00tracking topic evaluation detection tdt <span style="color:#555555;">Jonathan G. <a href="http://www.bibsonomy.org/author/Fiscus">Fiscus</a> and George <a href="http://www.bibsonomy.org/author/Doddington">Doddington</a> and John S. <a href="http://www.bibsonomy.org/author/Garofolo">Garofolo</a> and Alvin <a href="http://www.bibsonomy.org/author/Martin">Martin</a> </span><em>Proc. of the DARPA Broadcast News Workshop, </em><em>Virginia, US, </em>(<em>1998</em>)Mon Apr 21 13:44:43 CEST 2008Virginia, USProc. of the DARPA Broadcast News WorkshopNIST's 1998 Topic Detection and Tracking Evaluation (TDT2)1998tracking topic evaluation detection tdt dblphttp://www.bibsonomy.org/bibtex/28ca69f31e7ab051f5576807162bf31bb/renewrenew2008-04-21T13:05:32+02:00detection temporal topic tdt <span style="color:#555555;">Juha <a href="http://www.bibsonomy.org/author/Makkonen">Makkonen</a> and Helena <a href="http://www.bibsonomy.org/author/Ahonen-Myka">Ahonen-Myka</a> and Marko <a href="http://www.bibsonomy.org/author/Salmenkivi">Salmenkivi</a> </span><em>ECIR, </em><em>volume2633ofLecture Notes in Computer Science, </em><em>page251-265. </em><em>Springer, </em>(<em>2003</em>)Mon Apr 21 13:05:32 CEST 2008ECIRconf/ecir/2003251-265Lecture Notes in Computer ScienceTopic Detection and Tracking with Spatio-Temporal Evidence.26332003detection temporal topic tdt dblphttp://www.bibsonomy.org/bibtex/247d9bbc0a4295471084bffaf29c75d74/renewrenew2008-04-21T13:02:44+02:00tdt temporal detection topic <span style="color:#555555;">Juha <a href="http://www.bibsonomy.org/author/Makkonen">Makkonen</a> and Helena <a href="http://www.bibsonomy.org/author/Ahonen-Myka">Ahonen-Myka</a> </span><em>ECDL, </em><em>volume2769ofLecture Notes in Computer Science, </em><em>page393-404. </em><em>Springer, </em>(<em>2003</em>)Mon Apr 21 13:02:44 CEST 2008ECDLconf/ercimdl/2003393-404Lecture Notes in Computer ScienceUtilizing Temporal Information in Topic Detection and Tracking.27692003tdt temporal detection topic http://www.bibsonomy.org/bibtex/2f81bc9315be5c3fb426ee22b2508c4d6/renewrenew2008-04-21T12:58:08+02:00tdt visualization evolution topic <span style="color:#555555;">Ata <a href="http://www.bibsonomy.org/author/Kaban">Kaban</a> and Mark <a href="http://www.bibsonomy.org/author/Girolami">Girolami</a> </span><em>Journal of Intelligent Information Systems</em><em>18(2/3):107--125</em>(<em>2002</em>)Mon Apr 21 12:58:08 CEST 2008Journal of Intelligent Information Systems2/3107--125A Dynamic Probabilistic Model to Visualise Topic Evolution in Text Streams182002tdt visualization evolution topic We propose a novel probabilistic method, based on latent variable models, for unsupervised topographic visualisation of dynamically evolving, coherent textual information. This can be seen as a complementary tool for topic detection and tracking applications. This is achieved by the exploitation of the a priori domain knowledge available, that there are relatively homogeneous temporal segments in the data stream. In a different manner from topographical techniques previously utilized for static text collections, the topography is an outcome of the coherence in time of the data stream in the proposed model. Simulation results on both toy-data settings and an actual application on Internet chat line discussion analysis is presented by way of demonstration.Topic discovery based on text mining techniqueshttp://www.bibsonomy.org/bibtex/25095275ddb32f4138ee5018a6a4f3fc6/renewrenew2008-02-23T00:37:49+01:00topic mining detection text <span style="color:#555555;">Aurora <a href="http://www.bibsonomy.org/author/Pons-Porrata">Pons-Porrata</a> and Rafael <a href="http://www.bibsonomy.org/author/Berlanga-Llavori">Berlanga-Llavori</a> and Jos'e <a href="http://www.bibsonomy.org/author/Ruiz-Shulcloper">Ruiz-Shulcloper</a> </span><em>Inf. Process. Manage.</em><em>43(3):752--768</em>(<em>2007</em>)Sat Feb 23 00:37:49 CET 2008Tarrytown, NY, USAInf. Process. Manage.3752--768Topic discovery based on text mining techniques432007topic mining detection text In this paper, we present a topic discovery system aimed to reveal the implicit knowledge present in news streams. This knowledge is expressed as a hierarchy of topic/subtopics, where each topic contains the set of documents that are related to it and a summary extracted from these documents. Summaries so built are useful to browse and select topics of interest from the generated hierarchies. Our proposal consists of a new incremental hierarchical clustering algorithm, which combines both partitional and agglomerative approaches, taking the main benefits from them. Finally, a new summarization method based on Testor Theory has been proposed to build the topic summaries. Experimental results in the TDT2 collection demonstrate its usefulness and effectiveness not only as a topic detection system, but also as a classification and summarization tool.dblphttp://www.bibsonomy.org/bibtex/2c0663174db640752e756736e636e0cf9/renewrenew2008-02-22T02:37:01+01:00detection topic email <span style="color:#555555;">Gabor <a href="http://www.bibsonomy.org/author/Cselle">Cselle</a> and Keno <a href="http://www.bibsonomy.org/author/Albrecht">Albrecht</a> and Roger <a href="http://www.bibsonomy.org/author/Wattenhofer">Wattenhofer</a> </span><em>Intelligent User Interfaces, </em><em>page190-197. </em><em>ACM, </em>(<em>2007</em>)Fri Feb 22 02:37:01 CET 2008Intelligent User Interfacesconf/iui/2007190-197BuzzTrack: topic detection and tracking in email.2007detection topic email http://www.bibsonomy.org/bibtex/220fb4bab61662864357a9edf960a9b9b/renewrenew2008-02-22T02:35:49+01:00topic <span style="color:#555555;">Mark <a href="http://www.bibsonomy.org/author/Steyvers">Steyvers</a> and Padhraic <a href="http://www.bibsonomy.org/author/Smyth">Smyth</a> and Michal <a href="http://www.bibsonomy.org/author/Rosen-Zvi">Rosen-Zvi</a> and Thomas <a href="http://www.bibsonomy.org/author/Griffiths">Griffiths</a> </span><em>KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, </em><em>page306--315. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2004</em>)Fri Feb 22 02:35:49 CET 2008New York, NY, USAKDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining306--315Probabilistic author-topic models for information discovery2004topic We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words for that topic. The words in a multi-author paper are assumed to be the result of a mixture of each authors' topic mixture. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to a large corpus of 160,000 abstracts and 85,000 authors from the well-known CiteSeer digital library, and learn a model with 300 topics. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, significant trends in the computer science literature between 1990 and 2002, parsing of abstracts by topics and authors and detection of unusual papers by specific authors. An online query interface to the model is also discussed that allows interactive exploration of author-topic models for corpora such as CiteSeer.