Multi-metric Graph Query Performance Prediction

2018 ◽  
pp. 289-306
Author(s):  
Keyvan Sasani ◽  
Mohammad Hossein Namaki ◽  
Yinghui Wu ◽  
Assefaw H. Gebremedhin
Keyword(s):  
Performance Prediction ◽  
Query Performance ◽  
Metric Graph ◽  
Graph Query

Query performance prediction for microblog search

2017 ◽  
Vol 53 (6) ◽  
pp. 1320-1341 ◽  
Author(s):  
Maram Hasanain ◽  
Tamer Elsayed
Keyword(s):  
Performance Prediction ◽  
Query Performance ◽  
Microblog Search

scholarly journals Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction

2021 ◽  
Author(s):  
Arabzadehghahyazi Negar
Keyword(s):  
Performance Prediction ◽  
State Of The Art ◽  
Learning To Rank ◽  
The State ◽  
Test Collection ◽  
Query Performance ◽  
Performance Predictors ◽  
Level Statistics ◽  
Ablation Study ◽  
Individual Specificity

file:///C:/Users/MWF/Downloads/Arabzadehghahyazi, Negar.Pre-retrieval Query Performance Prediction (QPP) methods are oblivious to the performance of the retrieval model as they predict query difficulty prior to observing the set of documents retrieved for the query. Among pre-retrieval query performance predictors, specificity-based metrics investigate how corpus, query and corpus-query level statistics can be used to predict the performance of the query. In this thesis, we explore how neural embeddings can be utilized to define corpus-independent and semantics-aware specificity metrics. Our metrics are based on the intuition that a term that is closely surrounded by other terms in the embedding space is more likely to be specific while a term surrounded by less closely related terms is more likely to be generic. On this basis, we leverage geometric properties between embedded terms to define four groups of metrics: (1) neighborhood-based, (2) graph-based, (3) cluster-based and (4) vector-based metrics. Moreover, we employ learning-to-rank techniques to analyze the importance of individual specificity metrics. To evaluate the proposed metrics, we have curated and publicly share a test collection of term specificity measurements defined based on Wikipedia category hierarchy and DMOZ taxonomy. We report on our extensive experiments on the effectiveness of our metrics through metric comparison, ablation study and comparison against the state-of-the-art baselines. We have shown that our proposed set of pre-retrieval QPP metrics based on the properties of pre-trained neural embeddings are more effective for performance prediction compared to the state-of-the-art methods. We report our findings based on Robust04, ClueWeb09 and Gov2 corpora and their associated TREC topics.

Query Performance Prediction Through Retrieval Coherency

2021 ◽  
pp. 193-200
Author(s):  
Negar Arabzadeh ◽  
Amin Bigdeli ◽  
Morteza Zihayat ◽  
Ebrahim Bagheri
Keyword(s):  
Performance Prediction ◽  
Query Performance

An Enhanced Evaluation Framework for Query Performance Prediction

2021 ◽  
pp. 115-129
Author(s):  
Guglielmo Faggioli ◽  
Oleg Zendel ◽  
J. Shane Culpepper ◽  
Nicola Ferro ◽  
Falk Scholer
Keyword(s):  
Performance Prediction ◽  
Evaluation Framework ◽  
Query Performance

scholarly journals Query Performance Prediction for Information Retrieval Based on Covering Topic Score

2008 ◽  
Vol 23 (4) ◽  
pp. 590-601 ◽  
Author(s):  
Hao Lang ◽  
Bin Wang ◽  
Gareth Jones ◽  
Jin-Tao Li ◽  
Fan Ding ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Performance Prediction ◽  
Query Performance
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