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.