Terrier Components

On this page we will give an overview of Terrier's main components and their interaction.

Component Interaction

The graphic below gives an overview of the interaction between the main components involved in the indexing process.

A corpus will be represented in the form of a Collection object. Raw text data will be represented in the form of a Document object. Document implementations usually are provided with an instance of a Tokeniser class that breaks pieces of text into single indexing tokens.
The indexer is responsible for managing the indexing process. It iterates over the documents of the collection and sends each term found through a TermPipeline component.
A TermPipeline can transform terms or remove terms that should not be indexed. An example for a TermPipeline chain is termpipelines=Stopwords,PorterStemmer, which removes terms from the document using the Stopwords object, and then applies Porter's Stemming algorithm for English to the terms (PorterStemmer).
Once terms have been processed through the TermPipeline, they are aggregated and the following data structures are created by their corresponding DocumentBuilders: DirectIndex, DocumentIndex, Lexicon, and InvertedIndex.
For single-pass indexing, the structures are written in a different order. Inverted file postings are built in memory, and committed to 'runs' when memory is exhausted. Once the collection had been indexed, all runs are merged form the inverted index and the lexicon.

The graphic below gives an overview of the interaction of Terrier's components in the retrieval phase.

An application, such as for example the Desktop Terrier or TrecTerrier applications, issues a query to the Terrier framework.
In a first step the query will be parsed and an instantiation of a Query object will take place.
Afterwards, the query will be handed to the Manager component. The manager firstly pre-processes the query, by applying it to the configured TermPipeline.
After the Pre-Processing the query will be handed to the Matching component. The Matching component is responsible for initialising the appropriate WeightingModel and DocumentScoreModifiers. Once all these components have been instantiated, the computation of document scores with respect to the query will take place.
Afterwards, the PostProcessing and PostFiltering takes place. In PostProcessing, the ResultSet can be altered in any way - for example, QueryExpansion expands the query, and then calls Matching again to generate an improved ranking of documents. PostFiltering is simpler, allowing documents to be either included or excluded - this is ideal for interactive applications where users want to restrict the domain of the documents being retrieved.
Finally, the ResultSet will be returned to the application component.

Here we provide a listing and brief description of Terrier's components.

Name	Description
Collection	This component encapsulates the most fundamental concept to indexing with Terrier - a Collection i.e. a set of documents. See org.terrier.indexing.Collection for more details.
Document	This component encapsulates the concept of a document. It is essentially an Iterator over terms in a document. See org.terrier.indexing.Document for more details.
Tokeniser	Used by Document objects to break sequences of text (e.g. sentences) into a stream of words to index. See org.terrier.indexing.tokenisation.Tokeniser for more details.
TermPipeline	Models the concept of a component in a pipeline of term processors. Classes that implement this interface could be stemming algorithms, stopwords removers, or acronym expansion just to mention few examples. See org.terrier.terms.TermPipeline for more details.
Indexer	The component responsible for managing the indexing process. It instantiates TermPipelines and Builders. See org.terrier.structures.indexing.Indexer for more details.
Builders	Builders are responsible for writing an index to disk. See org.terrier.structures.indexing package for more details.

Name	Description
BitFile	A highly compressed I/O layer using gamma and unary encodings. See the org.terrier.compression packages for more details.
Direct Index	The direct index stores the identifiers of terms that appear in each document and the corresponding frequencies. It is used for automatic query expansion, but can also be used for user profiling activities. See org.terrier.structures.bit.DirectIndex for more details.
Document Index	The document index stores information about each document for example the document length and identifier, and a pointer to the corresponding entry in the direct index. See org.terrier.structures.DocumentIndex for more details.
Inverted Index	The inverted index stores the posting lists, i.e. the identifiers of the documents and their corresponding term frequencies. Moreover it is capable of storing the position of terms within a document. See org.terrier.structures.bit.InvertedIndex for more details.
Lexicon	The lexicon stores the collection vocabulary and the corresponding document and term frequencies. See org.terrier.structures.Lexicon for more details.
Meta Index	The Meta Index stores additional (meta) information about each document, for example its unique textual identifier (docno) or URL. See org.terrier.structures.MetaIndex for more details.

Name	Description
Manager	This component is responsible for handling/coordinating the main high-level operations of a query. These are: Pre Processing (Term Pipeline, Control finding, term aggregation) Matching Post-processing Post-filtering See org.terrier.querying.Manager for more details.
Matching	The matching component is responsible for determining which documents match a specific query and for scoring documents with respect to a query. See org.terrier.matching.Matching for more details.
Query	The query component models a query, that consists of sub-queries and query terms. See org.terrier.querying.parser.Query for more details.
Weighting Model	The Weighting model represents the retrieval model that is used to weight the terms of a document. See org.terrier.matching.models.WeightingModel for more details.
Document Score Modifiers	Responsible for query dependent modification document scores. See org.terrier.matching.dsms package for more details.

Name	Description
Trec Terrier	An application that enables indexing and querying of TREC collections. See org.terrier.applications.TrecTerrier for more details.
Desktop Terrier	An application that allows for indexing and retrieval of local user content. See org.terrier.applications.desktop package for more details.
HTTP Terrier	An application that allows for retrieval of documents from a browser. See src/webapps/results.jsp for more details, or the relevant documentation.