text_indexing #

Dart library for creating an inverted index on a collection of text documents.

THIS PACKAGE IS PRE-RELEASE AND SUBJECT TO DAILY BREAKING CHANGES.

Objective #

The objective of this package is to provide an interface and implementation classes that build and maintain a term dictionary that holds the vocabulary of terms and the frequency of occurrence for each term in the corpus and a postings map that holds the references to the documents for each term. In this implementation, our postings include the positions of the term in the documents to allow search algorithms to derive relevance on a per document basis.

Definitions #

The following definitions are used throughout the documentation:

• corpus- the collection of documents for which an index is maintained.
• dictionary - is a hash of terms (vocabulary) to the frequency of occurence in the corpus documents. In this implementation, Dictionary is a type defintion for a hashmap with the vocabulary as key and the document frequency as the values.
• document - a record in the corpus, that has a unique identifier (docId) in the corpus's primary key and that contains one or more text fields that are indexed.
• index - an inverted index used to look up document references from the corpus against a vocabulary of terms. The implementation in this package builds and maintains a positional inverted index, that also includes the positions of the indexed term in each document.
• postings - a separate index that records which documents the vocabulary occurs in. In this implementation we also record the positions of each term in the document to create a positional inverted index.
• postings list - a record of the positions of a term in a document. A position of a term refers to the index of the term in an array that contains all the terms in the text.
• term - a word or phrase that is indexed from the corpus. The term may differ from the actual word used in the corpus depending on the tokenizer used.
• text - the indexable content of a document.
• token - representation of a term in a text source returned by a tokenizer. The token may include information about the term such as its position(s) in the text or frequency of occurrence.
• tokenizer - a function that returns a collection of tokens from text, after applying a character filter, term filter, stemmer and / or lemmatizer.
• vocabulary is the collection of terms/words indexed from the corpus.

Interface #

The text indexing classes (indexers) in this library inherit from TextIndexer, an interface intended for information retrieval software applications. The TextIndexer interface is consistent with information retrieval theory.

The inverted index is comprised of two artifacts:

• a Dictionary is a hashmap of DictionaryEntrys with the vocabulary as key and the document frequency as the values; and
• a Postings a hashmap of PostingsEntrys with the vocabulary as key and the postings lists for the linked documents as values.

The Dictionary and Postings can be asynchronous data sources or in-memory hashmaps. The TextIndexer reads and writes to/from these artifacts using the loadTerms, updateDictionary, loadTermPostings and upsertTermPostings asynchronous methods.

The index method indexes text from a document, returning a list of PostingsList that is also emitted by postingsStream. The index method calls emit, passing the list of PostingsList.

The emit method is called by index, and adds an event to the postingsStream.

Listen to postingsStream to update your dictionary and postings map.

Implementing classes override the following fields:

• Tokenizer is the Tokenizer instance used by the indexer to parse documents to tokens;
• postingsStream emits a list of PostingsList instances whenever a document is indexed.

Implementing classes override the following asynchronous methods:

• index indexes text from a document, returning a list of PostingsList and adding it to the postingsStream by calling emit;
• emit is called by index, and adds an event to the postingsStream after updating the Dictionary and Postings;
• loadTerms returns a Dictionary for a vocabulary from a Dictionary;
• updateDictionary passes new or updated DictionaryEntry instances for persisting to a Dictionary;
• loadTermPostings returns PostingsEntry entities for a vocabulary from Postings; and
• upsertTermPostings passes new or updated PostingsEntry instances for upserting to Postings.

References #

• Manning, Raghavan and Schütze, "Introduction to Information Retrieval", Cambridge University Press. 2008
• University of Cambridge, 2016 "Information Retrieval", course notes, Dr Ronan Cummins
• Wikipedia (1), "Inverted Index", from Wikipedia, the free encyclopedia
• Wikipedia (2), "Lemmatisation", from Wikipedia, the free encyclopedia
• Wikipedia (3), "Stemming", from Wikipedia, the free encyclopedia

Install #

In the pubspec.yaml of your flutter project, add the following dependency:

dependencies:
  text_indexing: ^0.0.1

In your code file add the following import:

import 'package:text_indexing/text_indexing.dart';

Usage #

Examples are provided for the InMemoryIndexer and PersistedIndexer, two implementations of the TextIndexer interface that inherit from TextIndexerBase.

TextIndexerBase Class #

The TextIndexerBase is an abstract base class that implements the TextIndexer.index and TextIndexer.emit methods.

Subclasses of TextIndexerBase may override the override TextIndexerBase.emit method to perform additional actions whenever a document is indexed.

InMemoryIndexer Class #

The InMemoryIndexer is a subclass of TextIndexerBase that builds and maintains in-memory Dictionary and PostingMap hashmaps. These hashmaps are updated whenever InMemoryIndexer.emit is called at the end of the InMemoryIndexer.index method, so awaiting a call to InMemoryIndexer.index will provide access to the updated InMemoryIndexer.dictionary and InMemoryIndexer.postings collections.

The InMemoryIndexer is suitable for indexing a smaller corpus. An example of the use of InMemoryIndexer is included in the examples.

PersistedIndexer Class #

The PersistedIndexer is a subclass of TextIndexerBase that asynchronously reads and writes dictionary and postings data sources. These data sources are asynchronously updated whenever PersistedIndexer.emit is called by the PersistedIndexer.index method.

The PersistedIndexer is suitable for indexing and searching a large corpus but may incur some latency penalty and processing overhead. An example of the use of PersistedIndexer is included in the package examples.

Issues #

If you find a bug please fill an issue.

This project is a supporting package for a revenue project that has priority call on resources, so please be patient if we don't respond immediately to issues or pull requests.