Terrier 1.1.0

Terrier project is a probabilistic Java toolkit for building search engines.
Terrier is software for the rapid development of Web, intranet, and desktop search engines.
More generally, it is a modular platform for building large-scale information retrieval applications, providing indexing and probabilistic retrieval functionalities.
It comes with a desktop search application.
Terrier has various cutting-edge features including parameter-free probabilistic retrieval approaches (such as Divergence from Randomness models), automatic query expansion/re-formulation methodologies, and efficient data compression techniques.
Terrier comes with a powerful proof-of-concept Desktop search application [Screenshots], and full TREC capabilities including the ability to index, query and evaluate the standard TREC collections, such as AP, WSJ, WT10G, .GOV and .GOV2.
Terrier is written in Java [Requirements] and has been successfully used for adhoc retrieval, Web search and cross-language retrieval, in a centralised or distributed setting.
Currently, it is also being used for running various applications.
Main features:
- Open Source (Mozilla Public Licence)
- Written in cross-platform Java
- Highly compressed disk data structures.
- Handling large-scale document collections.
- Direct file for efficient query expansion.
- Modular and open indexing and querying APIs.
- Testbed for indexing and retrieval from standard TREC test collections.
- Interactive querying application.
- Desktop search application for searching various types of documents.
- Input/output of gamma, unary and binary encoded integers for compressing streams or random access files.
- Standard evaluation of TREC ad-hoc and known-item search retrieval results.
- Indexing of tagged document collections, as well as documents of various formats, such as HTML, PDF, or Microsoft Word, Excel and Powerpoint files.
- Indexing of field information.
- Indexing of position information on a word, or a block level.
- Support for classic retrieval models, such as tf-idf, BM25 and Ponte-Croft language model, and Rocchios query expansion.
- Provides a number of Divergence From Randomness (DFR) document ranking models.
- Provides a number of parameter-free DFR term weighting models for automatic query expansion.
- Advanced query language that supports AND/NOT operators, phrase and proximity search.
- Flexible processing of terms through a pipeline of components, such as stop-words removers and stemmers.
Enhancements:
- This is a major update with improvements in indexing and retrieval functionalities, including faster indexing and retrieval, and new retrieval models (including models from Divergence from Randomness and Language modeling).
- It has support for much larger collections of documents, including TREC GOV2 collections (25M documents), merging of indices, and multi-lingual and non-English collections of documents.
- The documentation has been vastly improved.