About: Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction
Creator	Bagheri, Ebrahim Arabzadeh, Negar Jovanovic, Jelena Zarrinkalam, Fattane
Source	PMC
abstract	Query Performance Prediction (QPP) is concerned with estimating the effectiveness of a query within the context of a retrieval model. It allows for operations such as query routing and segmentation, leading to improved retrieval performance. Pre-retrieval 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. Since neural embedding-based models are showing wider adoption in the Information Retrieval (IR) community, we propose a set of pre-retrieval QPP metrics based on the properties of pre-trained neural embeddings and show that such metrics are more effective for query performance prediction compared to the widely known QPP metrics such as SCQ, PMI and SCS. We report our findings based on Robust04, ClueWeb09 and Gov2 corpora and their associated TREC topics.
has issue date	2020-03-24(xsd:dateTime)
bibo:doi	10.1007/978-3-030-45442-5_10
has license	no-cc
sha1sum (hex)	f74f85921d46042eed9918c08a142cacb4dea9c9
schema:url	https://doi.org/10.1007/978-3-030-45442-5_10
resource representing a document's title	Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction
has PubMed Central identifier	PMC7148077
schema:publication	Advances in Information Retrieval
resource representing a document's body	covid:f74f85921d46042eed9918c08a142cacb4dea9c9#body_text
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