Pyserini is a Python toolkit for reproducible information retrieval research with sparse and
dense representations. It aims to provide effective, reproducible, and easy-to-use first-stage …

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 …

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 …

This work tackles the perennial problem of reproducible baselines in information retrieval
research, focusing on bag-of-words ranking models. Although academic information …

The advent of deep machine learning platforms such as Tensorflow and Pytorch, developed
in expressive high-level languages such as Python, have allowed more expressive …

PyTerrier is a Python-based retrieval framework for expressing simple and complex
information retrieval (IR) pipelines in a declarative manner. While making use of the long …

Improving the quality of search results can significantly enhance users experience and
engagement with search engines. In spite of several recent advancements in the fields of …

Query expansion aims to mitigate the mismatch between the language used in a query and
in a document. However, query expansion methods can suffer from introducing non-relevant …

Word embeddings and convolutional neural networks (CNN) have attracted extensive
attention in various classification tasks for Twitter, eg sentiment classification. However, the …

For many complex diseases, there is no “one size fits all” solutions for patients with a
particular diagnosis. The proper treatment for a patient depends upon genetic …