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» Learning to rank for information retrieval (LR4IR 2008)
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CLEF
2008
Springer
15 years 8 months ago
Using Visual Concepts and Fast Visual Diversity to Improve Image Retrieval
In this article, we focus our efforts (i) on the study of how to automatically extract and exploit visual concepts and (ii) on fast visual diversity. First, in the Visual Concept D...
Sabrina Tollari, Marcin Detyniecki, Ali Fakeri-Tab...
TOCHI
2010
90views more  TOCHI 2010»
15 years 4 months ago
Potential for personalization
Current Web search tools do a good job of retrieving documents that satisfy the wide range of intentions that people associate with a query – but do not do a very good job of di...
Jaime Teevan, Susan T. Dumais, Eric Horvitz
KDD
2004
ACM
162views Data Mining» more  KDD 2004»
16 years 6 months ago
Discovering Links Between Lexical and Surface Features in Questions and Answers
Information retrieval systems, based on keyword match, are evolving to question answering systems that return short passages or direct answers to questions, rather than URLs point...
Soumen Chakrabarti
WSDM
2010
ACM
251views Data Mining» more  WSDM 2010»
16 years 3 months ago
Large Scale Query Log Analysis of Re-Finding
Although Web search engines are targeted towards helping people find new information, people regularly use them to re-find Web pages they have seen before. Researchers have noted ...
Jaime Teevan, Sarah K. Tyler
ICIW
2008
IEEE
16 years 17 days ago
XML Semantic Filtering via Ontology Reasoning
In this paper, we present an extension of PHIL, a declarative language for filtering information from XML data. The proposed approach allows us to extract relevant data as well a...
Michele Baggi, Moreno Falaschi, Demis Ballis