This paper provides an extensive and thorough overview of the models and techniques
utilized in the first and second stages of the typical information retrieval processing chain …

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …

High-quality medical systematic reviews require comprehensive literature searches to
ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for …

This paper outlines a conceptual framework for understanding recent developments in
information retrieval and natural language processing that attempts to integrate dense and …

Search engines operate under a strict time constraint as a fast response is paramount to
user satisfaction. Thus, neural reranking models have a limited time-budget to re-rank …

Learning-to-Rank models based on additive ensembles of regression trees have been
proven to be very effective for scoring query results returned by large-scale Web search …

As information retrieval researchers, we not only develop algorithmic solutions to hard
problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on …

Text Classification pipelines are a sequence of tasks needed to be performed to classify
documents into a set of predefined categories. The pre-processing phase (before training) of …

Information Retrieval (IR) and Recommender Systems (RSs) tasks are moving from
computing a ranking of final results based on a single metric to multi-objective problems …

Complex machine learning models are now an integral part of modern, large-scale retrieval
systems. However, collection size growth continues to outpace advances in efficiency …