Terrier IR Platform
2.2.1

Terrier Information Retrieval Platform version 2.2.1 API Specification

Terrier is a modular platform for the rapid development of large-scale Information Retrieval applications, providing indexing and retrieval functionalities.

See:
          Description

Packages
uk.ac.gla.terrier.applications Provides application-level code that use the Terrier platform to perform indexing and retrieval from either standard test collections, interactive querying of a indexed collection, or desktop search.
uk.ac.gla.terrier.applications.desktop Provides an Java Swing application, based on Terrier, that can be used for searching the desktop of a user.
uk.ac.gla.terrier.applications.desktop.filehandling Provides the functionality of opening arbitrary files from the Java Swing-based desktop search application.
uk.ac.gla.terrier.compression Provides implementation of a random access file and input and output streams where we can read or write gamma, unary and binary encoded integers.
uk.ac.gla.terrier.evaluation Provides an interface and the classes to process relevance assessments and perform standard evaluation of retrieval results.
uk.ac.gla.terrier.indexing Provides classes and interfaces related to the indexing of documents.
uk.ac.gla.terrier.indexing.hadoop  
uk.ac.gla.terrier.matching Provides the classes and interfaces used for matching documents to queries.
uk.ac.gla.terrier.matching.dsms Provides the interface and the classes for modifying the scores of documents after an score has been assigned to documents, or implementing the combination of evidence.
uk.ac.gla.terrier.matching.models Provides the classes that implement various classical IR models, as well as models from the Divergence From Randomness (DFR) framework.
uk.ac.gla.terrier.matching.models.aftereffect Provides the interface and the classes for implementing the term frequency normalisation component in the DFR framework.
uk.ac.gla.terrier.matching.models.basicmodel Provides the interface and the classes for implementing the basic models for randomness in the DFR framework.
uk.ac.gla.terrier.matching.models.languagemodel Provides the classes that implement language models in Terrier.
uk.ac.gla.terrier.matching.models.normalisation Provides the interface and the classes for implementing the basic models for randomness in the DFR framework.
uk.ac.gla.terrier.matching.models.queryexpansion Provides the classes that implement various query expansion models.
uk.ac.gla.terrier.matching.tsms Provides the interface and classes that implement the term score modifiers, which modify the scores assigned to documents for a particular term.
uk.ac.gla.terrier.querying Provides the interfaces and classes for the querying API of the Terrier platform, the controls, post processors and filters.
uk.ac.gla.terrier.querying.parser Provides the parser specification and the classes that implement the query language of the Terrier platform.
uk.ac.gla.terrier.sorting Provides the classes that implement the sorting of various arrays for the Terrier platform.
uk.ac.gla.terrier.structures Provides the classes that implement the data structures used for retrieval with the Terrier platform.
uk.ac.gla.terrier.structures.indexing Provides the classes used for creating the data structures of the Terrier platform.
uk.ac.gla.terrier.structures.indexing.singlepass Provides implementation of the structures needed for performing a single pass indexing
uk.ac.gla.terrier.structures.indexing.singlepass.hadoop  
uk.ac.gla.terrier.structures.merging Provides classes for merging two sets of data structures, created by Terrier, into one set of data structures.
uk.ac.gla.terrier.structures.upgrading  
uk.ac.gla.terrier.terms Provides the interface and classes for the term pipeline, a set of objects that process the terms during indexing and processing of queries.
uk.ac.gla.terrier.utility Provides the interface and classes for the term pipeline, a set of objects that process the terms during indexing and processing of queries.
uk.ac.gla.terrier.utility.io  

 

Terrier is a modular platform for the rapid development of large-scale Information Retrieval applications, providing indexing and retrieval functionalities. Terrier is based on the Divergence from Randomness (DFR) framework. It can index various document collections, including the standard TREC collections, such as AP, WSJ, WT10G, .GOV and .GOV2. It also provides a wide range of parameter-free weighting approaches and full-text search algorithms, aiming to offer a public testbed for performing Information Retrieval experiments.


Terrier IR Platform
2.2.1

Terrier Information Retrieval Platform 2.2.1. Copyright 2004-2008 University of Glasgow