In this work, we propose a novel framework to devise features that can be used by Query
Performance Prediction (QPP) models for Neural Information Retrieval (NIR). Using the …

Query Performance Prediction is a key task in IR, focusing on estimating the retrieval quality
of a given query without relying on human-labeled relevance judgments. Over the decades …

Query Performance Prediction (QPP) is focused on estimating the difficulty of satisfying a
user query for a certain retrieval method. While most state of the art QPP methods are based …

Query performance prediction (QPP) is a core task in information retrieval (IR) that aims at
predicting the retrieval quality for a given query without relevance judgments. QPP has been …

The use of clarifying questions (CQs) is a fairly new and useful technique to aid systems in
recognizing the intent, context, and preferences behind user queries. Yet, understanding the …

Estimating query difficulty, also known as Query Performance Prediction (QPP), is
concerned with assessing the retrieval quality of a ranking method for an input query. Most …

Thanks to recent advances in IR and NLP, the way users interact with search engines is
evolving rapidly, with multi-turn conversations replacing traditional one-shot textual queries …

Researchers have shown that the retrieval effectiveness of queries may depend on other
factors in addition to the semantics of the query. In other words, several queries expressed …

Research has already found that many retrieval methods are sensitive to the choice and
order of terms that appear in a query, which can significantly impact retrieval effectiveness …

The ad hoc retrieval task aims at ranking relevant documents to a user query such that the
most relevant documents are ranked higher compared to less relevant ones. Given the …