Configuring Indexing in Terrier

Indexing Overview

The indexing process in Terrier is described here

Firstly, a Collection object extracts the raw content of each individual document (from a collection of documents) and hands it in to a Document object. The Document object then removes any unwanted content (e.g., from a particular document tag) and gives the resulting text to a Tokeniser object. Finally, the Tokeniser converts the text into a stream of tokens that represent the content of the document.

By default, Terrier uses TRECCollection, which parses corpora in TREC format. In particular, in TREC-formatted files, there are many documents delimited by <DOC></DOC> tags, as in the following example:

<DOC>
<DOCNO> doc1 </DOCNO>
Content of the document does here
</DOC>
<DOC>
...

For corpora in other formats, you will need to change the Collection object being used, by setting the property trec.collection.class. Here are some options:

Except for the special-purpose collections (SimpleFileCollection, SimpleXMLCollection, and SimpleMedlineXMLCollection), all other Collection implementations allow for different Document implementations to be used, by specifying the trec.document.class property. By default, these collections use TaggedDocument. The available Document implementations are:

Finally, all Document implementations can specify their own Tokeniser implementation. By default, Terrier uses the EnglishTokeniser. When indexing non-English corpora, a different Tokeniser implementation can be specified by the tokeniser property.

Basic indexing setup

For now, we’ll stick to TRECCollection, which can be used for all TREC corporas from Disks 1&2 until Blogs08, including WT2G, .GOV, .GOV2, etc. TRECCollection can be further configured.

Note that the specified tags are case-sensitive, but this can be relaxed by setting the TrecDocTags.casesensitive property to false. Furthermore, TRECCollection also supports the addition of the contents of tags to the meta index. This is useful if you wish to present these during retrieval (e.g. the URL of the document, or the date). To use this, the tags in the TREC collection file need to be in a fixed order, beginning with the DOC and DOCNO tags, followed by the tags to be added to the meta index and specified by the TrecDocTags.propertytags property. Any tags occurring after the property tags will be indexed as if they contain text (unless excluded by TrecDocTags.skip). The name of the entries in the meta index must be the same as the tag names. Moreover, as with any entries added to the meta index, these entries must be specified in the indexer.meta.forward.keys property and the maximum length of each tag must be given in the indexer.meta.forward.keylens property.

Fields

Terrier has the ability to record the frequency with which terms occur in various fields of documents. The required fields are specified by the FieldTags.process property. For example, to note when a term occurs in the TITLE or H1 HTML tags of a document, set FieldTags.process=TITLE,H1. FieldTags are case-insensitive. There is a special field called ELSE, which contains all terms not in any other specified field.

The indexer iterates through the documents of the collection and sends each term found through the TermPipeline. The TermPipeline transforms the terms, and can remove terms that should not be indexed. The TermPipeline chain in use 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). If you want to use a different stemmer, this is the point at which it should be called.

The term pipeline can also be configured at indexing time to skip various tokens. Set a comma-delimited list of tokens to skip in the property termpipelines.skip. The same property works at retrieval time also.

The indexers are more complicated. Each class can be configured by several properties.

For the BlockIndexer:

Once terms have been processed through the TermPipeline, they are aggregated by the DocumentPostingList and the LexiconMap. These have a few properties:

Document metadata is recorded in a MetaIndex structure. For instance, such metadata could include the DOCNO and URL of each document, which the system can use to represent the document during retrieval. The MetaIndex can be configured to take note of various document attributes during indexing. The available attributes depend on those provided by the Document implementation. MetaIndex can be configured using the following properties:

Note that for presenting results to a user, additional indexing configuration is required. See Web-based Terrier for more information.

Choice of Indexers

Terrier supports three types of indexing: classical two-pass, single-pass and MapReduce. All three methods create an identical inverted index, that produces identical retrieval effectiveness. However, they differ on other characteristics, namely their support for query expansion, and the scalability and efficiency when indexing large corpora. The choice of indexing method is likely to be driven by your need for query expansion, and the scale of the data you are working with. In particular, only classical two-pass indexing directly creates a direct index, which is used for query expansion. However, classical two-pass indexing doesn't scale to large corpora (maximum practical is about 25 million documents). Single pass indexing is faster, but doesn't create a direct index. MapReduce indexing can be used when you have very large data (e.g. 50+ million documents), and already have an existing Hadoop cluster. If you do create an index that doesn't have a direct index, you can create one later using --inverted2direct option of TrecTerrier

Classical two-pass indexing

Classical indexing works by creating a direct index, and then inverting that data structure to create an inverted index. For details on the implementation of classical indexing, see the indexing implementation documentation.

Single-pass indexing

Single-pass indexing is implemented by the classes BasicSinglePassIndexer and BlockSinglePassIndexer. Essentially, instead of building a direct file from the collection, term posting lists are held in memory, and written to disk when memory is exhausted. The final step merged the temporary files to form the lexicon and the inverted file. Notably, single-pass indexing does not build a direct index. However, a direct index can be build later using the --inverted2direct command line argument to TrecTerrier.

For details on the implementation of single-pass indexing, see the indexing implementation documentation.

Threaded indexing

Starting from version 4.2, Terrier has experimental support for indexing using multiple threads. This can be enabled using -P option to TrecTerrier when specifying `-i. Both single-pass and classical indexing are supported by threaded indexing. The number of threads used is equal to the number of CPU cores in the machine, minus one.

MapReduce indexing

For large-scale collections, Terrier provides a MapReduce based indexing system. For more details, please see Hadoop MapReduce Indexing with Terrier.

Real-time indexing

Terrier also supports the real-time indexing of document collections using MemoryIndex and IncrementalIndex structures, allowing for new documents to be added to the index at later points in time. For more details, please see Real-time Index Structures.

Compression

By default, Terrier uses Elias-Gamma and Elias-Unary algorithms for ensuring a highly compressed direct and inverted indices, however starting with version 4.0 Terrier now has support for a variety of state-of-the-art compression schemes including PForDelta. For more information about configuring the compression used for indexing, see the documentation on compression.

More about Block Indexing

What are blocks?

A block is a unit of text in a document. When you index using blocks, you tell Terrier to save positional information with each term. Depending on how Terrier has been configured, a block can be of size 1 or larger. Size 1 means that the exact position of each term can be determined. For size > 1, the block id is incremented after every N terms. You can configure the size of a block using the property blocks.size.

How do I use blocks?

You can enable block indexing by setting the property block.indexing to true in your terrier.properties file. This ensures that the Indexer used for indexing is the BlockIndexer, not the BasicIndexer (or BlockSinglePassIndexer instead of BasicSinglePassIndexer). When loading an index, Terrier will detect that the index has block information saved and use the appropriate classes for reading the index files.

You can use the positional information when doing retrieval. For instance, you can search for documents matching a phrase, e.g. Terabyte retriever, or where the words occur near each other, e.g. indexing blocks~20.

What changes when I use block indexing?

When you enable the property block.indexing, TrecTerrier will use the BlockIndexer, not the BasicIndexer (if you have specified single-pass indexing, it is the BlockSinglePassIndexer, not the BasicSinglePassIndexer that is used). The created index data structures will contain the positions for each posting, and can be read by, and when accessed through PostingIndex.getPostings() will implement BlockPosting in addition to IterablePosting.

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