Indexing Word Sequences for Ranked Retrieval

2014 ◽  
Vol 32 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Samuel Huston ◽  
J. Shane Culpepper ◽  
W. Bruce Croft
Keyword(s):  
Ranked Retrieval

scholarly journals Scalable ranked retrieval using document images

2013 ◽  
Author(s):  
Rajiv Jain ◽  
Douglas W. Oard ◽  
David Doermann
Keyword(s):  
Document Images ◽  
Ranked Retrieval

Secure multi-keyword search supporting dynamic update and ranked retrieval

2016 ◽  
Vol 13 (10) ◽  
pp. 209-221 ◽  
Author(s):  
Jingbo Yan ◽  
Yuqing Zhang ◽  
Xuefeng Liu
Keyword(s):  
Keyword Search ◽  
Dynamic Update ◽  
Ranked Retrieval

scholarly journals Using Zero-Resource Spoken Term Discovery for Ranked Retrieval

2015 ◽  
Author(s):  
Jerome White ◽  
Douglas Oard ◽  
Aren Jansen ◽  
Jiaul Paik ◽  
Rashmi Sankepally
Keyword(s):  
Ranked Retrieval

Refining Ranked Retrieval Results for Legal Discovery Search Through Supervised Rank Aggregation

2013 ◽  
Author(s):  
Brian Almquist ◽  
Padmini Srinivasan
Keyword(s):  
Data Mining ◽  
Rank Aggregation ◽  
Mining System ◽  
Nous Concluons ◽  
Ranked Retrieval ◽  
Data Mining System ◽  
Legal Discovery

We propose and evaluate a data mining system that uses a set of document features describing each document in the context of partially evaluated ranked results. We find our system to be competitive with existing metasearch ranking strategies for prioritizing the review of evidence for legal relevance.Nous proposons et évaluons un système de fouille de données basé sur une série de descripteurs de documents décrivant chaque document dans un contexte d’évaluation partielle des résultats classés. Nous concluons que notre système est concurrentiel par rapport aux stratégies existantes de classement des métarecherches pour la priorisation de l’examen des preuves en matière de pertinence juridique. 

scholarly journals Information Retrieval and Graph Analysis Approaches for Book Recommendation

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Chahinez Benkoussas ◽  
Patrice Bellot
Keyword(s):  
Information Retrieval ◽  
Directed Graph ◽  
Language Model ◽  
Social Information ◽  
Link Analysis ◽  
Graph Analysis ◽  
Retrieval Models ◽  
Xml Retrieval ◽  
Ranked Retrieval

A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments.

Sign in / Sign up

Export Citation Format

Share Document