Abstract
Terrier is a modular platform for the rapid development of large-scale Information Retrieval (IR) applications. It can index various document collections, including TREC and Web collections. Terrier also offers a range of document weighting and query expansion models, based on the Divergence From Randomness framework. It has been successfully used for ad-hoc retrieval, cross-language retrieval, Web IR and intranet search, in a centralised or distributed setting.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ounis, I., Amati, G., Plachouras, V., He, B., Macdonald, C., Johnson, D. (2005). Terrier Information Retrieval Platform. In: Losada, D.E., Fernández-Luna, J.M. (eds) Advances in Information Retrieval. ECIR 2005. Lecture Notes in Computer Science, vol 3408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31865-1_37
Download citation
DOI: https://doi.org/10.1007/978-3-540-31865-1_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25295-5
Online ISBN: 978-3-540-31865-1
eBook Packages: Computer ScienceComputer Science (R0)