LEADER 07990nam 22008775 450 001 9910484987503321 005 20251226203648.0 010 $a1-280-38807-2 010 $a9786613565990 010 $a3-642-14556-6 024 7 $a10.1007/978-3-642-14556-8 035 $a(CKB)2550000000015610 035 $a(SSID)ssj0000446469 035 $a(PQKBManifestationID)11312201 035 $a(PQKBTitleCode)TC0000446469 035 $a(PQKBWorkID)10496507 035 $a(PQKB)11270281 035 $a(DE-He213)978-3-642-14556-8 035 $a(MiAaPQ)EBC3065496 035 $a(PPN)149072694 035 $a(BIP)31291389 035 $a(EXLCZ)992550000000015610 100 $a20100707d2010 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aFocused Retrieval and Evaluation $e8th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2009, Brisbane, Australia, December 7-9, 2009, Revised and Selected Papers /$fedited by Shlomo Geva, Jaap Kamps, Andrew Trotman 205 $a1st ed. 2010. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2010. 215 $a1 online resource (XII, 466 p. 59 illus.) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v6203 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-642-14555-8 320 $aIncludes bibliographical references and index. 327 $aInvited -- Is There Something Quantum-Like about the Human Mental Lexicon? -- Supporting for Real-World Tasks: Producing Summaries of Scientific Articles Tailored to the Citation Context -- Semantic Document Processing Using Wikipedia as a Knowledge Base -- Ad Hoc Track -- Overview of the INEX 2009 Ad Hoc Track -- Analysis of the INEX 2009 Ad Hoc Track Results -- ENSM-SE at INEX 2009 : Scoring with Proximity and Semantic Tag Information -- LIP6 at INEX?09: OWPC for Ad Hoc Track -- A Methodology for Producing Improved Focused Elements -- ListBM: A Learning-to-Rank Method for XML Keyword Search -- UJM at INEX 2009 Ad Hoc Track -- Language Models for XML Element Retrieval -- Use of Language Model, Phrases and Wikipedia Forward Links for INEX 2009 -- Parameter Tuning in Pivoted Normalization for XML Retrieval: ISI@INEX09 Adhoc Focused Task -- Combining Language Models with NLP and Interactive Query Expansion -- Exploiting Semantic Tags in XML Retrieval -- Book Track -- Overview of the INEX 2009 Book Track -- XRCE Participation to the 2009 Book Structure Task -- The Book Structure Extraction Competition with the Resurgence Software at Caen University -- Ranking and Fusion Approaches for XML Book Retrieval -- OUC?s Participation in the 2009 INEX Book Track -- Efficiency Track -- Overview of the INEX 2009 Efficiency Track -- Index Tuning for Efficient Proximity-Enhanced Query Processing -- TopX 2.0 at the INEX 2009 Ad-Hoc and Efficiency Tracks -- Fast and Effective Focused Retrieval -- Achieving High Precisions with Peer-to-Peer Is Possible! -- Entity Ranking Track -- Overview of the INEX 2009 Entity Ranking Track -- Combining Term-Based and Category-Based Representations for Entity Search -- Focused Search in Books and Wikipedia: Categories, Links and Relevance Feedback -- A Recursive Approach to EntityRanking and List Completion Using Entity Determining Terms, Qualifiers and Prominent n-Grams -- Interactive Track -- Overview of the INEX 2009 Interactive Track -- Link the Wiki Track -- Overview of the INEX 2009 Link the Wiki Track -- An Exploration of Learning to Link with Wikipedia: Features, Methods and Training Collection -- University of Waterloo at INEX 2009: Ad Hoc, Book, Entity Ranking, and Link-the-Wiki Tracks -- A Machine Learning Approach to Link Prediction for Interlinked Documents -- Question Answering Track -- Overview of the 2009 QA Track: Towards a Common Task for QA, Focused IR and Automatic Summarization Systems -- XML Mining Track -- Overview of the INEX 2009 XML Mining Track: Clustering and Classification of XML Documents -- Exploiting Index Pruning Methods for Clustering XML Collections -- Multi-label Wikipedia Classification with Textual and Link Features -- Link-Based Text Classification Using Bayesian Networks -- Clustering with Random Indexing K-tree and XML Structure -- Utilising Semantic Tags in XML Clustering -- UJM at INEX 2009 XML Mining Track -- BUAP: Performance of K-Star at the INEX?09 Clustering Task -- Extended VSM for XML Document Classification Using Frequent Subtrees -- Supervised Encoding of Graph-of-Graphs for Classification and Regression Problems. 330 $aWelcome to the proceedings of the 8th Workshop of the Initiative for the Eva- ation of XML Retrieval (INEX)! Now in its eighth year, INEX is an established evaluation forum for XML information retrieval (IR), with over 100 organi- tions worldwide registered and over 50 groups participating actively in at least one of the tracks. INEX aims to provide an infrastructure, in the form of a large structured test collection and appropriate scoring methods, for the evaluation of focused retrieval systems. XML IR plays an increasingly important role in many information access systems (e.g., digitallibraries,Web, intranet) where content is a mixture oftext, multimedia, and metadata, formatted according to the adopted W3C standard for information repositories, the so-called eXtensible Markup Language (XML). The ultimate goal of such systems is to provide the right content to their e- users. However, while many of today's information access systems still treat documents as single large (text) blocks, XML o'ers the opportunity to exploit the internal structure of documents in order to allow for more precise access, thus providing more speci'c answers to user requests. Providing e'ective access to XML-based content is therefore a key issue for the success of these systems. INEX2009wasanexcitingyearforINEXinwhichanewcollectionwasint- duced that is again based on Wikipedia but is more than four times larger, with longerarticlesandadditionalsemanticannotation.Intotal,eightresearchtracks were included, which studied di'erent aspects of focused information access: Ad Hoc Track investigatedthee'ectivenessofXML-IRandPassageRetrieval for four ad hoc retrievaltasks:Thorough,Focused, Relevant in Context, and Best in Context. 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v6203 606 $aData mining 606 $aInformation storage and retrieval systems 606 $aDatabase management 606 $aApplication software 606 $aInformation retrieval 606 $aComputer architecture 606 $aArtificial intelligence$xData processing 606 $aData Mining and Knowledge Discovery 606 $aInformation Storage and Retrieval 606 $aDatabase Management 606 $aComputer and Information Systems Applications 606 $aData Storage Representation 606 $aData Science 615 0$aData mining. 615 0$aInformation storage and retrieval systems. 615 0$aDatabase management. 615 0$aApplication software. 615 0$aInformation retrieval. 615 0$aComputer architecture. 615 0$aArtificial intelligence$xData processing. 615 14$aData Mining and Knowledge Discovery. 615 24$aInformation Storage and Retrieval. 615 24$aDatabase Management. 615 24$aComputer and Information Systems Applications. 615 24$aData Storage Representation. 615 24$aData Science. 676 $a006.7/4 701 $aGeva$b Shlomo$0927693 701 $aKamps$b Jaap$0887141 701 $aTrotman$b Andrew$0980148 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484987503321 996 $aFocused retrieval and evaluation$94195337 997 $aUNINA