Some methods have been developed for automatic effectiveness evaluation without relevance judgments. We propose to use those methods, and their combination based on a machine learning approach, for query performance prediction. Moreover, since predicting average precision as it is usually done in query performance prediction literature is sensitive to the reference system that is chosen, we focus on predicting the average of average precision values over several systems. Results of an extensive experimental evaluation on ten TREC collections show that our proposed methods outperform state-of-the-art query performance predictors
Query performance prediction methods are usually applied to estimate the retrieval effectiveness of ...
Query performance prediction (QPP) aims at automatically estimating the information retrieval system...
Query difficulty prediction aims to identify, in advance, how well an information retrieval system w...
International audienceSome methods have been developed for automatic effectiveness evaluation withou...
Query performance predictors are commonly evaluated by reporting correlation coefficients to denote ...
Numerous studies have examined the ability of query performance prediction methods to estimate a que...
Query performance predictors are commonly evaluated by reporting correlation coefficients to denote ...
In this paper, we examine a number of newly applied methods for combining pre-retrieval query perfor...
In this thesis we consider users' attempts to express their information needs through queries, or se...
In this paper, we examine a number of newly applied methods for combining pre-retrieval query perfor...
The query performance prediction (QPP) task is to estimate the effectiveness of a search performed i...
There has been much work on devising query-performance prediction approaches that estimate search ef...
The query-performance prediction (QPP) task is estimat-ing retrieval effectiveness with no relevance...
Query performance prediction aims to predict whether a query will have a high average precision give...
The query-performance prediction task has been described as estimating retrieval effectiveness in th...
Query performance prediction methods are usually applied to estimate the retrieval effectiveness of ...
Query performance prediction (QPP) aims at automatically estimating the information retrieval system...
Query difficulty prediction aims to identify, in advance, how well an information retrieval system w...
International audienceSome methods have been developed for automatic effectiveness evaluation withou...
Query performance predictors are commonly evaluated by reporting correlation coefficients to denote ...
Numerous studies have examined the ability of query performance prediction methods to estimate a que...
Query performance predictors are commonly evaluated by reporting correlation coefficients to denote ...
In this paper, we examine a number of newly applied methods for combining pre-retrieval query perfor...
In this thesis we consider users' attempts to express their information needs through queries, or se...
In this paper, we examine a number of newly applied methods for combining pre-retrieval query perfor...
The query performance prediction (QPP) task is to estimate the effectiveness of a search performed i...
There has been much work on devising query-performance prediction approaches that estimate search ef...
The query-performance prediction (QPP) task is estimat-ing retrieval effectiveness with no relevance...
Query performance prediction aims to predict whether a query will have a high average precision give...
The query-performance prediction task has been described as estimating retrieval effectiveness in th...
Query performance prediction methods are usually applied to estimate the retrieval effectiveness of ...
Query performance prediction (QPP) aims at automatically estimating the information retrieval system...
Query difficulty prediction aims to identify, in advance, how well an information retrieval system w...