http://www.bibsonomy.org/burst/user/hotho/textBibSonomy BuRST Feed for /user/hotho/text2008-07-21T01:12:12+02:00D5 MPI-INF Publications: Proceedings Article: Learning Word-to-Concept Mappings for Automatic Text Classificationhttp://www.bibsonomy.org/bibtex/257f8241941ed979455c3dbb90893020f/hothohotho2008-07-01T15:19:39+02:00topic tc classification text concept wordnet model <span style="color:#555555;">Georgiana <a href="http://www.bibsonomy.org/author/Ifrim">Ifrim</a> and Martin <a href="http://www.bibsonomy.org/author/Theobald">Theobald</a> and Gerhard <a href="http://www.bibsonomy.org/author/Weikum">Weikum</a> </span><em>Proceedings of the 22nd International Conference on Machine Learning - Learning in Web Search (LWS 2005), </em><em>page18--26. </em><em>Bonn, Germany, </em>(<em>2005</em>)Tue Jul 01 15:19:39 CEST 2008Bonn, GermanyProceedings of the 22nd International Conference on Machine Learning - Learning in Web Search (LWS 2005)18--26Learning Word-to-Concept Mappings for Automatic Text Classification2005topic tc classification text concept wordnet model http://www.bibsonomy.org/bibtex/22d7c9ea5484ee45ea8bf3520138d7477/hothohotho2008-04-17T10:10:02+02:00ol learning fca myown text 2005 SumSchool06 <span style="color:#555555;">P. <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> and A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and S. <a href="http://www.bibsonomy.org/author/Staab">Staab</a> </span><em>Journal of Artificial Intelligence Research (JAIR)</em>(<em>2005</em>)Thu Apr 17 10:10:02 CEST 2008Journal of Artificial Intelligence Research (JAIR)305-339Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis242005ol learning fca myown text 2005 SumSchool06 Amazon.de: Text Mining. Predictive Methods for Analyzing Unstructured Information: Sholom M. Weiss,Nitin Indurkhya,T. Zhang: English Bookshttp://www.bibsonomy.org/bibtex/26ac07561b543e6033fd4c9811d0dccad/hothohotho2008-04-12T15:25:20+02:00text software dm nlp mining tm <span style="color:#555555;">Sholom M. <a href="http://www.bibsonomy.org/author/Weiss">Weiss</a> and Nitin <a href="http://www.bibsonomy.org/author/Indurkhya">Indurkhya</a> and T. <a href="http://www.bibsonomy.org/author/Zhang">Zhang</a> </span><em>Springer, Berlin, </em><em>1 edition, </em>(<em>2004</em>)Sat Apr 12 15:25:20 CEST 20081Text Mining. Predictive Methods for Analyzing Unstructured Information2004text software dm nlp mining tm SpringerLink - Book Chapterhttp://www.bibsonomy.org/bibtex/2121bdc75ba2f61db089bac5f715a07ba/hothohotho2008-02-28T10:16:38+01:00clustering myown ol 2006 text classification <span style="color:#555555;">Stephan <a href="http://www.bibsonomy.org/author/Bloehdorn">Bloehdorn</a> and Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>From Data and Information Analysis to Knowledge Engineering</em>(<em>2006</em>)Thu Feb 28 10:16:38 CET 2008From Data and Information Analysis to Knowledge Engineering334--341Learning Ontologies to Improve Text Clustering and Classification2006clustering myown ol 2006 text classification Recent work has shown improvements in text clustering and classification tasks by integrating conceptual features extracted from ontologies. In this paper we present text mining experiments in the medical domain in which the ontological structures used are acquired automatically in an unsupervised learning process from the text corpus in question. We compare results obtained using the automatically learned ontologies with those obtained using manually engineered ones. Our results show that both types of ontologies improve results on text clustering and classification tasks, whereby the automatically acquired ontologies yield a improvement competitive with the manually engineered ones. ER -http://www.bibsonomy.org/bibtex/2e472dc4e61921ed15175756fcd9fea6a/hothohotho2008-02-02T10:31:29+01:00text clustering classification <span style="color:#555555;">L. <a href="http://www.bibsonomy.org/author/Douglas Baker">Douglas Baker</a> and Andrew K. <a href="http://www.bibsonomy.org/author/McCallum">McCallum</a> </span><em>Proceedings of SIGIR-98, 21st ACM International Conference on Research and Development in Information Retrieval, </em><em>page96--103. </em><em>Melbourne, AU, </em><em>ACM Press, New York, US, </em>(<em>1998</em>)Sat Feb 02 10:31:29 CET 2008Melbourne, AUProceedings of {SIGIR}-98, 21st {ACM} International Conference on Research and Development in Information Retrieval96--103Distributional clustering of words for text classification1998text clustering classification Text Clustering Based on Good Aggregationshttp://www.bibsonomy.org/bibtex/2a6803e87c5145d5f55d7bb1bab8dfd67/hothohotho2008-01-28T15:00:01+01:00tm text clustering 2001 ontology myown gruppenbildung kmeans <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Alexander <a href="http://www.bibsonomy.org/author/Maedche">Maedche</a> and Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> </span><em>ICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining, </em><em>page607--608. </em><em>Washington, DC, USA, </em><em>IEEE Computer Society, </em>(<em>2001</em>)Mon Jan 28 15:00:01 CET 2008Washington, DC, USAICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining607--608Text Clustering Based on Good Aggregations2001tm text clustering 2001 ontology myown gruppenbildung kmeans http://www.bibsonomy.org/bibtex/218fdbebb76d48feccf2dceed23f4cd74/hothohotho2008-01-15T11:04:28+01:002002 ontology clustering text myown <span style="color:#555555;">A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and G. <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of FGML Workshop, </em><em>page37-45. </em><em>Special Interest Group of German Informatics Society (FGML --- Fachgruppe Maschinelles Lernen der GI e.V.), </em>(<em>2002</em>)Tue Jan 15 11:04:28 CET 2008Proceedings of FGML Workshop37-45Conceptual Clustering of Text Clusters20022002 ontology clustering text myown http://www.bibsonomy.org/bibtex/256ae0afc93999014629b06cc958f6a04/hothohotho2008-01-15T11:03:13+01:002002 text mining myown <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Alexander <a href="http://www.bibsonomy.org/author/Maedche">Maedche</a> and Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> and Valentin <a href="http://www.bibsonomy.org/author/Zacharias">Zacharias</a> </span><em>Proc. of Text Mining Workshop, </em>(<em>2002</em>)Tue Jan 15 11:03:13 CET 2008Proc. of Text Mining WorkshopOn Knowledgeable Unsupervised Text Mining 20022002 text mining myown http://www.bibsonomy.org/bibtex/23bc6e5a51dba862da1b7b3b6ac563370/hothohotho2008-01-15T10:15:04+01:00text clustering 2004 myown ontology ol <span style="color:#555555;">Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> </span><em>Proceedings of the Conference on Languages Resources and Evaluation (LREC), </em><em>Lisbon, Portugal, </em><em>ELRA - European Language Ressources Association, </em><em>MAY2004. </em>Tue Jan 15 10:15:04 CET 2008Lisbon, PortugalProceedings of the Conference on Languages Resources and Evaluation (LREC)MAYClustering Ontologies from Text2004text clustering 2004 myown ontology ol http://www.bibsonomy.org/bibtex/28b298c325c6ecdb9c01e01057464ae2d/hothohotho2008-01-15T10:00:15+01:00classification myown agrovoc text ontology 2003 SumSchool06 <span style="color:#555555;">Boris <a href="http://www.bibsonomy.org/author/Lauser">Lauser</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>Proc. of the 7th European Conference in Research and Advanced Technology for Digital Libraries, ECDL 2003, </em><em>volume2769ofLNCS, </em><em>page140-151. </em><em>Springer, </em>(<em>2003</em>)Tue Jan 15 10:00:15 CET 2008Proc. of the 7th European Conference in Research and Advanced Technology for Digital Libraries, ECDL 2003140-151LNCSAutomatic multi-label subject indexing in a multilingual environment27692003classification myown agrovoc text ontology 2003 SumSchool06 http://www.bibsonomy.org/bibtex/2c1bb26aa5d4801542f832ffab70c82e5/hothohotho2008-01-15T09:57:54+01:00fca text 2003 clustering visualization SumSchool06 myown <span style="color:#555555;">A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and S. <a href="http://www.bibsonomy.org/author/Staab">Staab</a> and G. <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proc. of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD, </em><em>volume2838ofLNCS, </em><em>page217-228. </em>(<em>2003</em>)Tue Jan 15 09:57:54 CET 2008Proc. of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD217-228LNCSExplaining Text Clustering Results using Semantic Structures28382003fca text 2003 clustering visualization SumSchool06 myown http://www.bibsonomy.org/bibtex/2e297ea25cc01678c35501a44115aea8d/hothohotho2008-01-15T09:46:14+01:002006 myown text classification boosting <span style="color:#555555;">Stephan <a href="http://www.bibsonomy.org/author/Bloehdorn">Bloehdorn</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>Advances in Web Mining and Web Usage Analysis, </em><em>volume3932ofLNCS, </em><em>Springer, </em>(<em>2006</em>)Tue Jan 15 09:46:14 CET 2008Advances in Web Mining and Web Usage Analysis149--166LNCSBoosting for Text Classification with Semantic Features393220062006 myown text classification boosting dblphttp://www.bibsonomy.org/bibtex/2d773061117a913428968cc99c6e1ec0f/hothohotho2008-01-13T12:15:27+01:00ontology myown text clustering 2003 <span style="color:#555555;">Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>Intelligent Information Processing and Web Mining, Proceedings of the International IIS: IIPWM'03 Conference held in Zakopane, </em><em>page451-452. </em>(<em>2003</em>)Sun Jan 13 12:15:27 CET 2008Intelligent Information Processing and Web Mining, Proceedings of the International IIS: IIPWM'03 Conference held in Zakopane451-452Ontology-based Text Document Clustering.2003ontology myown text clustering 2003 ScienceDirect - Technological Forecasting and Social Change : Literature-related discovery (LRD): Potential treatments for cataractshttp://www.bibsonomy.org/bibtex/2b9359f79985da9b9677340ffda849e74/hothohotho2007-12-30T11:00:21+01:00mining semantic clustering discovery toread retrieval text <span style="color:#555555;">Ronald N. <a href="http://www.bibsonomy.org/author/Kostoff">Kostoff</a> </span><em>Technological Forecasting and Social Change</em>(<em>2007</em>)Sun Dec 30 11:00:21 CET 2007Technological Forecasting and Social Change--Literature-related discovery (LRD): Potential treatments for cataractsIn Press, Corrected Proof2007mining semantic clustering discovery toread retrieval text Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. This paper describes the second medical problem we addressed (cataract) using ODS LRD; the first problem addressed was Raynaud's Phenomenon (RP), and was described in the third paper of this Special Issue. Cataract was selected because it is ubiquitous globally, appears intractable to all forms of treatment other than surgical removal of cataracts, and is a major cause of blindness in many developing countries. The ODS LRD study had three objectives: a) identify non-drug non-surgical treatments that would 1) help prevent cataracts, or 2) reduce the progression rate of cataracts, or 3) stop the progression of cataracts, or 4) maybe even reverse the progression of cataracts; b) demonstrate that we could solve an ODS LRD problem with no prior knowledge of any results or prior work (unlike the case with the RP problem); c) determine whether large time savings in the discovery process were possible relative to the time required for conducting the RP study. To that end, we used the MeSH taxonomy of MEDLINE to restrict potential discoveries to selected semantic classes, as a substitute for the manually-intensive process used in the RP study to restrict potential discoveries to selected semantic classes. We also used additional semantic filtering to identify potential discovery within the selected semantic classes. All these goals were achieved. As will be shown, we generated large amounts of potential discovery in more than an order of magnitude less time than required for the RP study. We identified many non-drug non-surgical treatments that may be able to reduce or even stop the progression rate of cataracts. Time, and much testing, will determine whether this is possible. Finally, the methodology has been developed to the point where ODS LRD problems can be solved with no results or knowledge of any prior work.http://www.bibsonomy.org/bibtex/2c7aff853599cdde58a1d27eff4ede314/hothohotho2007-09-03T16:56:35+02:00tm text model ir <span style="color:#555555;">Chengxiang <a href="http://www.bibsonomy.org/author/Zhai">Zhai</a> and John <a href="http://www.bibsonomy.org/author/Lafferty">Lafferty</a> </span><em>ACM Trans. Inf. Syst.</em><em>22(2):179--214</em>(<em>2004</em>)Mon Sep 03 16:56:35 CEST 2007New York, NY, USAACM Trans. Inf. Syst.2179--214A study of smoothing methods for language models applied to information retrieval222004tm text model ir Language modeling approaches to information retrieval are attractive and promising because they connect the problem of retrieval with that of language model estimation, which has been studied extensively in other application areas such as speech recognition. The basic idea of these approaches is to estimate a language model for each document, and to then rank documents by the likelihood of the query according to the estimated language model. A central issue in language model estimation is smoothing, the problem of adjusting the maximum likelihood estimator to compensate for data sparseness. In this article, we study the problem of language model smoothing and its influence on retrieval performance. We examine the sensitivity of retrieval performance to the smoothing parameters and compare several popular smoothing methods on different test collections. Experimental results show that not only is the retrieval performance generally sensitive to the smoothing parameters, but also the sensitivity pattern is affected by the query type, with performance being more sensitive to smoothing for verbose queries than for keyword queries. Verbose queries also generally require more aggressive smoothing to achieve optimal performance. This suggests that smoothing plays two different role---to make the estimated document language model more accurate and to "explain" the noninformative words in the query. In order to decouple these two distinct roles of smoothing, we propose a two-stage smoothing strategy, which yields better sensitivity patterns and facilitates the setting of smoothing parameters automatically. We further propose methods for estimating the smoothing parameters automatically. Evaluation on five different databases and four types of queries indicates that the two-stage smoothing method with the proposed parameter estimation methods consistently gives retrieval performance that is close to---or better than---the best results achieved using a single smoothing method and exhaustive parameter search on the test data.dblphttp://www.bibsonomy.org/bibtex/27cdd6b0791fcdf17ec6d404b55f12c5c/hothohotho2007-08-31T14:53:26+02:00classification document text 2003 tm srl mining <span style="color:#555555;">Alexandrin <a href="http://www.bibsonomy.org/author/Popescul">Popescul</a> and Lyle H. <a href="http://www.bibsonomy.org/author/Ungar">Ungar</a> and Steve <a href="http://www.bibsonomy.org/author/Lawrence">Lawrence</a> and David M. <a href="http://www.bibsonomy.org/author/Pennock">Pennock</a> </span><em>ICDM, </em><em>page275-282. </em><em>IEEE Computer Society, </em>(<em>2003</em>)Fri Aug 31 14:53:26 CEST 2007ICDMconf/icdm/2003275-282Statistical Relational Learning for Document Mining.2003classification document text 2003 tm srl mining http://www.bibsonomy.org/bibtex/2d1bb2e8dff9bd80da158b4b770685dce/hothohotho2007-08-23T15:12:04+02:00text tm mining <span style="color:#555555;">R. <a href="http://www.bibsonomy.org/author/Feldman">Feldman</a> and I. <a href="http://www.bibsonomy.org/author/Dagan">Dagan</a> </span><em>Proc. of the First Int. Conf. on Knowledge Discovery (KDD), </em><em>page112-117. </em>(<em>1995</em>)Thu Aug 23 15:12:04 CEST 2007Proc. of the First Int. Conf. on Knowledge Discovery (KDD)feldman95KDT112-117Knowledge Discovery in Textual Databases (KDT)InProceedings1995text tm mining http://www.bibsonomy.org/bibtex/2d15caaaea82b6df0747cc298a8b13556/hothohotho2007-07-04T09:35:22+02:00concept clustering hierarchies text <span style="color:#555555;">Mark <a href="http://www.bibsonomy.org/author/Sanderson">Sanderson</a> and William Bruce <a href="http://www.bibsonomy.org/author/Croft">Croft</a> </span><em>Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'99, </em><em>page206--213. </em>(<em>1999</em>)Wed Jul 04 09:35:22 CEST 2007Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'99206--213Deriving concept hierarchies from text1999concept clustering hierarchies text Ontology Learning and Population from Texthttp://www.bibsonomy.org/bibtex/2fdfff52cddb448c0094213aff5bcaf31/hothohotho2007-06-05T12:00:48+02:00text ol <span style="color:#555555;">Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> </span><em>Springer-Verlag New York, Inc., </em><em>Secaucus, NJ, USA, </em>(<em>2006</em>)Tue Jun 05 12:00:48 CEST 2007Secaucus, NJ, USAOntology Learning and Population from Text: Algorithms, Evaluation and Applications2006text ol A Comparison of Document Clustering Techniqueshttp://www.bibsonomy.org/bibtex/210e5c1e3ff54d9dce505a231f8ae7b32/hothohotho2007-01-09T09:03:22+01:00document bisec text hac kmeans clustering ***** <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Steinbach">Steinbach</a> and G. <a href="http://www.bibsonomy.org/author/Karypis">Karypis</a> and V. <a href="http://www.bibsonomy.org/author/Kumar">Kumar</a> </span><em>KDD Workshop on Text Mining, </em>(<em>2000</em>)Tue Jan 09 09:03:22 CET 2007KDD Workshop on Text MiningA comparison of document clustering techniques2000document bisec text hac kmeans clustering *****