A Python Interface to PISA!

MacAvaney, S. and Macdonald, C. (2022) A Python Interface to PISA! In: SIGIR 2022: 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11-15 Jul 2022, pp. 3339-3344. ISBN 9781450387323 (doi: 10.1145/3477495.3531656)

Abstract

PISA (Performant Indexes and Search for Academia) provides very efficient implementations of various retrieval algorithms over sparse inverted indices. The highly-optimized C++ implementation, however, has previously only been accessible via command line tools. From indexing to retrieval, 5--6 commands need to be executed in sequence, making the process relatively involved. Further complications when using PISA include a lengthy build process and minimal interoperability with other tools. In this work, we demonstrate a new tool that provides a native Python wrapper around PISA. The wrapper features a simplified interface that adheres to the PyTerrier API, making it easy to use (e.g., via Pandas DataFrames), apply to a multitude of datasets (e.g., those from the ir_datasets package) and combine with other methods (e.g., neural re-ranking and dense retrieval methods).

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: MacAvaney, Dr Sean and Macdonald, Professor Craig
• Authors: MacAvaney, S., and Macdonald, C.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450387323
• Copyright Holders: Copyright © 2022 Association for Computing Machinery
• First Published: First published in SIGIR 2022: 45th International ACM SIGIR Conference on Research and Development in Information Retrieval: 3339-3344
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Related URLs

• Organisation

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
300982		Exploiting Closed-Loop Aspects in Computationally and Data Intensive Analytics	Roderick Murray-Smith	Engineering and Physical Sciences Research Council (EPSRC)	EP/R018634/1	Computing Science