Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

Recent advances in Information Retrieval utilise energy-intensive hardware to produce state-
of-the-art results. In areas of research highly related to Information Retrieval, such as Natural …

Current query expansion models use pseudo-relevance feedback to improve first-pass
retrieval effectiveness; however, this fails when the initial results are not relevant. Instead of …

Pseudo-relevance feedback mechanisms, from Rocchio to the relevance models, have
shown the usefulness of expanding and reweighting the users' initial queries using …

We integrate irdatasets, ir_measures, and PyTerrier with TIRA in the Information Retrieval
Experiment Platform (TIREx) to promote more standardized, reproducible, scalable, and …

This paper presents ranx, a Python evaluation library for Information Retrieval built on top of
Numba. ranx provides a user-friendly interface to the most common ranking evaluation …

Web search is a crucial technology for the digital economy. Dominated by a few gatekeepers
focused on commercial success, however, web publishers have to optimize their content for …

Pretrained contextualized language models such as BERT and T5 have established a new
state-of-the-art for ad-hoc search. However, it is not yet well understood why these methods …

Search systems often employ a re-ranking pipeline, wherein documents (or passages) from
an initial pool of candidates are assigned new ranking scores. The process enables the use …

Technology-assisted review (TAR) is an important industrial application of information
retrieval (IR) and machine learning (ML). While a small TAR research community exists, the …