Welcome to the Terrier IR Platform

Terrier is a highly flexible, efficient, and effective open source search engine, readily deployable on large-scale collections of documents. Terrier implements state-of-the-art indexing and retrieval functionalities, and provides an ideal platform for the rapid development and evaluation of large-scale retrieval applications.

Terrier is open source, and is a comprehensive, flexible and transparent platform for research and experimentation in text retrieval. Research can easily be carried out on standard TREC and CLEF test collections.

Terrier is written in Java, and is developed at the School of Computing Science, University of Glasgow.


Feature Overview

Efficient

Terrier can index large corpora of documents, and provides multiple indexing strategies, such as multi-pass, single-pass and large-scale MapReduce indexing.

Effective

State-of-the-art retrieval approaches are provided, such as Divergence From Randomness, BM25F, as well as term dependence proximity models such as Markov Random Fields.

Flexible

Terrier is ideal for performing information retrieval experiments. It can index and perform batch retrieval experiments for all known TREC test collections. Tools to evaluate experiments results are also included.

Multi-lingual

Terrier uses UTF internally, and can support corporas written in languages other than English.

Extensible

Terrier follows a plugin architecture, and is easy to extend to develop new retrieval techniques.

Interactive

View search results in a handy desktop search application, or online from a JSP web interface.

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Engage

  • Learn More

    Terrier has extensive documentation about indexing and retrieval, as well as about running experiments using TREC test collections.

  • Ask

    Join the active community forum to ask for help with any part of Terrier, or update the wiki.

  • Contribute

    Found a bug, or want to improve Terrier? You can contribute to Terrier, by filing issues on the issue tracker.