Semantics-enabled query performance prediction for ad hoc table retrieval

Khodabakhsh, Maryam; Bagheri, Ebrahim

doi:10.1016/j.ipm.2020.102399

Cited by 14 publications

(4 citation statements)

References 15 publications

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“…Chifu et al [71] reported 312 values, none of which above 0.50. In the same way, Khodabakhsh and Bagheri report 952 correlation values, none of which are above 0.46 [73]. When correlation are low it is even likelier that there is either very weak or no correlation at all between the predicted value (here effectiveness) and the feature used to predict.…”

Section: Considering the Queries And Their Pre-and Post-retrieval Fea...mentioning

confidence: 86%

“…• Linguistic features extracted from the query only [21,[54][55][56]; • Other pre-retrieval features that use information on the document collection [57-61]; • Post retrieval features that consider the retrieved documents for that query [22,57,[62][63][64][65][66][67][68][69][70][71][72][73].…”

Section: Considering the Queries And Their Pre-and Post-retrieval Fea...mentioning

confidence: 99%

“…Finally, the query effect was also studied considering post-retrieval features [57,[62][63][64][65][66][67][69][70][71][72][73]. Post-retrieval predictors are categorised into clarity-based, score-based, and robustness-based approaches [22,68].…”

Section: Considering the Queries And Their Pre-and Post-retrieval Fea...mentioning

confidence: 99%

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Analytics Methods to Understand Information Retrieval Effectiveness—A Survey

Mothe

2022

Mathematics

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Information retrieval aims to retrieve the documents that answer users’ queries. A typical search process consists of different phases for which a variety of components have been defined in the literature; each one having a set of hyper-parameters to tune. Different studies focused on how and how much the components and their hyper-parameters affect the system performance in terms of effectiveness, others on the query factor. The aim of these studies is to better understand information retrieval system effectiveness. This paper reviews the literature of this domain. It depicts how data analytics has been used in IR for a better understanding system effectiveness. This review concludes lack of full understanding of system effectiveness according to the context although it has been possible to adapted the query processing to some contexts successfully. This review also concludes that, even if it is possible to distinguish effective from non effective system on average on a query set, nor the system component analysis nor the query features analysis were successful in explaining when and why a particular system fails on a particular query.

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Section: Considering the Queries And Their Pre-and Post-retrieval Fea...mentioning

confidence: 86%

Section: Considering the Queries And Their Pre-and Post-retrieval Fea...mentioning

confidence: 99%

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Analytics Methods to Understand Information Retrieval Effectiveness—A Survey

Mothe

2022

Mathematics

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“…Early research in QPP utilizes linguistic information [29], statistical features [15,23,24] in pre-retrieval methods, or analyses clarity [15,16], robustness [7,20,48,54,55], retrieval scores [34,41,44,47,55] for post-retrieval prediction, which further evolves into several effective frameworks [17,20,28,38,40,45,46]. The QPP techniques have also been explored and analyzed in [3,5,6,10,18,21,22,25,35,36,39,42,43,52,53,27]. With the recent development deep learning techniques, NeuralQPP [51] achieves promising results by training a three-components deep network under weak supervision of existing methods.…”

Section: Related Workmentioning

confidence: 99%

Groupwise Query Performance Prediction with BERT

Chen¹,

He²,

Sun³

2022

Preprint

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While large-scale pre-trained language models like BERT have advanced the state-of-the-art in IR, its application in query performance prediction (QPP) is so far based on pointwise modeling of individual queries. Meanwhile, recent studies suggest that the cross-attention modeling of a group of documents can effectively boost performances for both learning-to-rank algorithms and BERT-based re-ranking. To this end, a BERT-based groupwise QPP model is proposed, in which the ranking contexts of a list of queries are jointly modeled to predict the relative performance of individual queries. Extensive experiments on three standard TREC collections showcase effectiveness of our approach.

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