MultiText Legal Experiments at TREC 2007 Stefan Büttcher, Charles L. A. Clarke, Gordon V. Cormack, Thomas R. Lynam David R. Cheriton School of Computer Science University of Waterloo Waterloo, Ontario, Canada

First Page		Document Content
Date: 2008-02-11 08:18:55 Natural language processing Relevance feedback Information retrieval Okapi BM25 Ranking Document retrieval Search engine indexing Search engine N-gram Learning to rank		MultiText Legal Experiments at TREC 2007 Stefan Büttcher, Charles L. A. Clarke, Gordon V. Cormack, Thomas R. Lynam David R. Cheriton School of Computer Science University of Waterloo Waterloo, Ontario, Canada Add to Reading List Source URL: trec.nist.gov Download Document from Source Website File Size: 139,28 KB Share Document on Facebook

	Cascading Bandits: Learning to Rank in the Cascade Model Branislav Kveton Adobe Research, San Jose, CA Csaba Szepesv´ari Department of Computing Science, University of Alberta DocID: 1uHeV - View Document
	Learning to Rank By Aggregating Expert Preferences Maksims N. Volkovs Hugo Larochelle Richard S. Zemel DocID: 1tUcb - View Document
	McRank: Learning to Rank Using Multiple Classification and Gradient Boosting Ping Li ∗ Dept. of Statistical Science Cornell University DocID: 1tgDg - View Document
	Tagging data as implicit feedback for learning-to-rank Beate Navarro Bullock Robert Jäschke Andreas Hotho DocID: 1soOw - View Document
	Regression Rank: Learning to Meet the Opportunity of Descriptive Queries Matthew Lease1 , James Allan2, and Bruce Croft2 1 Brown Laboratory for Linguistic Information Processing (BLLIP) DocID: 1sm9K - View Document