Focused Retrieval and Evaluation: 8th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2009, Brisbane, Australia, December 7-9, 2009, Revised and Selected Papers: Lecture Notes in Computer Science, cartea 6203
Editat de Shlomo Geva, Jaap Kamps, Andrew Trotmanen Limba Engleză Paperback – 15 iul 2010
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Specificații
ISBN-13: 9783642145551
ISBN-10: 3642145558
Pagini: 464
Ilustrații: XII, 466 p. 59 illus.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.7 kg
Ediția:2010
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Information Systems and Applications, incl. Internet/Web, and HCI
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3642145558
Pagini: 464
Ilustrații: XII, 466 p. 59 illus.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.7 kg
Ediția:2010
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Information Systems and Applications, incl. Internet/Web, and HCI
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
Invited.- 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.
Caracteristici
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