Model-based Feedback in the Language Modeling Approach to Information Retrieval (2001)

by Chengxiang Zhai , John Lafferty
Venue:In Proceedings of Tenth International Conference on Information and Knowledge Management
Citations:174 - 19 self

Active Bibliography

35 Risk Minimization and Language Modeling in Text Retrieval – ChengXiang Zhai - 2002
3 A Brief Review of Information Retrieval Models – Chengxiang Zhai - 2007
1 Incorporating context into the language modeling for ad hoc information retrieval – Leif Azzopardi
Toward the Pricipled Utilization . . . – Leif Azzopardi
37 Term-specific smoothing for the language modeling approach to information retrieval: the importance of a query term – Djoerd Hiemstra - 2002
250 Parsimonious Language Models for Information Retrieval – Djoerd Hiemstra, Stephen Robertson, Hugo Zaragoza - 2004
47 A GENERATIVE THEORY OF RELEVANCE – Victor Lavrenko - 2004
47 A Risk Minimization Framework for Information Retrieval – ChengXiang Zhai , John Lafferty - 2003
4 Nymble: a high performance learning name-finder – Djoerd Hiemstra, Vojkan Mihajlović - 1997
1 Information theory – Rongmei Li, Rianne Kaptein, Djoerd Hiemstra, Jaap Kamps
9 Relating the New Language Models of Information Retrieval to the Traditional Retrieval Models – Djoerd Hiemstra, Arjen P. De Vries - 2000
201 Two-stage language models for information retrieval – Chengxiang Zhai - 2003
10 Optimal Mixture Models in IR – Victor Lavrenko - 2002
80 Pagerank without hyperlinks: structural re-ranking using links induced by language models – Oren Kurland, Lillian Lee - 2005
5 Conceptual language models for domain-specific retrieval – Edgar Meij , et al.
696 A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval – Chengxiang Zhai, John Lafferty
18 A General Optimization Framework for Smoothing Language Models on Graph Structures – Qiaozhu Mei, Duo Zhang, Chengxiang Zhai
8 Passage retrieval and evaluation – Courtney Wade, James Allan - 2005
24 Better than the real thing? Iterative pseudo-query processing using cluster-based language models – Oren Kurland, et al.