text_analysis 0.14.0 | Dart package

Tokenize text, compute document readbility and compare terms. #

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

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Overview
Usage
API
Definitions
References
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Overview #

The text_analysis package provides methods to tokenize text, compute readibility scores for a document and evaluate similarity of terms. It is intended to be used in Natural Language Processing (NLP) as part of an information retrieval system.

It is split into four (4) libraries:

text_analysis is the core library that exports the tokenization, analysis and string similarity functions;
extensions exports extension methods also provided as static methods of the TextSimilarity class;
package_exports exports the porter_2_stemmer package; and
type_definitions exports all the typedefs used in this package.

Refer to the references to learn more about information retrieval systems and the theory behind this library.

Tokenization

Tokenization comprises the following steps:

a term splitter splits text to a list of terms at appropriate places like white-space and mid-sentence punctuation;
a character filter manipulates terms prior to stemming and tokenization (e.g. changing case and / or removing non-word characters);
a term filter manipulates the terms by splitting compound or hyphenated terms or applying stemming and lemmatization. The termFilter can also filter out stopwords; and
the tokenizer converts the resulting terms to a collection of tokens that contain the term and a pointer to the position of the term in the source text.

A String extension method Set<KGram> kGrams([int k = 2]) that parses a set of k-grams of length k from a term. The default k-gram length is 3 (tri-gram).

Text analysis

Readibility #

The TextDocument enumerates a text document's paragraphs, sentences, terms and tokens and computes readability measures:

the average number of words in each sentence;
the average number of syllables for words;
the Flesch reading ease score, a readibility measure calculated from sentence length and word length on a 100-point scale; and
Flesch-Kincaid grade level, a readibility measure relative to U.S. school grade level.

String Comparison #

The following measures of term similarity are provided:

Damerau–Levenshtein distance is the minimum number of single-character edits (transpositions, insertions, deletions or substitutions) required to change one term into another;
edit similarity is a normalized measure of Damerau–Levenshtein distance on a scale of 0.0 to 1.0, calculated by dividing the the difference between the maximum edit distance (sum of the length of the two terms) and the computed editDistance, by the maximum edit distance;
length distance returns the absolute value of the difference in length between two terms;
length similarity returns the similarity in length between two terms on a scale of 0.0 to 1.0 on a log scale (1 - the log of the ratio of the term lengths);
Jaccard similarity measures similarity between finite sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets; and
termSimilarity returns a similarity index value between 0.0 and 1.0, product of edit similarity , Jaccard similarity and length similarity. A term similarity of 1.0 means the two terms are identical in all respects.

Functions that return the term similarity measures are provided by static methods of the TermSimilarity class.

Usage #

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

dependencies:
  text_analysis: <latest version>

In your code file add the text_analysis library import. This will also import the Porter2Stemmer class from the porter_2_stemmer package.

import 'package:text_analysis/text_analysis.dart';

To use the package's extensions and/or type definitions, also add any of the following imports:

import 'package:text_analysis/extensions.dart';
import 'package:text_analysis/type_definitions.dart';

Basic English tokenization can be performed by using a TextTokenizer instance with the default text analyzer and no token filter:

  // Use a TextTokenizer instance to tokenize the text using the default 
  // English analyzer.
  final document = await TextTokenizer().tokenize(text);

To analyze text or a document, hydrate a TextDocument to obtain the text statistics and readibility scores:

      // get some sample text
      final sample =
          'The Australian platypus is seemingly a hybrid of a mammal and reptilian creature.';

      // hydrate the TextDocument
      final textDoc = await TextDocument.analyze(sourceText: sample);

      // print the `Flesch reading ease score`
      print(
          'Flesch Reading Ease: ${textDoc.fleschReadingEaseScore().toStringAsFixed(1)}');
      // prints "Flesch Reading Ease: 37.5"

For more complex text analysis:

implement a TextAnalyzer for a different language or non-language documents;
implement a custom TextTokenizeror extend TextTokenizerBase; and/or
pass in a TokenFilter function to a TextTokenizer to manipulate the tokens after tokenization as shown in the examples; and/or extend TextDocumentBase.

To compare terms, call the required extension on the term, or the static method from the TermSimilarity class:


  // define a misspelt term
  const term = 'bodrer';

  // a collection of auto-correct options
  const candidates = [
    'bord',
    'board',
    'broad',
    'boarder',
    'border',
    'brother',
    'bored'
  ];

  // get a list of the terms orderd by descending similarity
  final matches = term.matches(candidates);
  // same as TermSimilarity.matches(term, candidates))

  // print matches
  print('Ranked matches: $matches');
  // prints:
  //     Ranked matches: [border, boarder, bored, brother, board, bord, broad]
  //

Please see the examples for more details.

API #

The key interfaces of the text_analysis library are briefly described in this section. Please refer to the documentation for details.

TermSimilarity

The TermSimilarity class provides the following static methods used for (case-insensitive) comparison of terms:

editDistance returns the Damerau–Levenshtein distance, the minimum number of single-character edits (transpositions, insertions, deletions or substitutions) required to change one term into another;
editSimilarity returns a normalized measure of Damerau–Levenshtein distance on a scale of 0.0 to 1.0, calculated by dividing the the difference between the maximum edit distance (sum of the length of the two terms) and the computed editDistance, by by the maximum edit distance;
lengthDistance returns the absolute value of the difference in length between two terms;
lengthSimilarity returns the similarity in length between two terms on a scale of 0.0 to 1.0 on a log scale (1 - the log of the ratio of the term lengths);
jaccardSimilarity returns the Jaccard Similarity Index of two terms;
termSimilarity returns a similarity index value between 0.0 and 1.0, product of editSimilarity , jaccardSimilarity and lengthSimilarity. A term similarity of 1.0 means the two terms are identical in all respects, except case;

To compare one term with a collection of other terms, the following methods are also provided:

editDistanceyMap returns a hashmap of terms to their editSimilarity with a term;
editSimilarityMap returns a hashmap of terms to their editSimilarity with a term;
lengthSimilarityMap returns a hashmap of terms to their lengthSimilarity with a term;
jaccardSimilarityMap returns a hashmap of terms to Jaccard Similarity Index with a term;
termSimilarityMap returns a hashmap of terms to termSimilarity with a term; and
matches returns the best matches from terms for a term, in descending order of term similarity (best match first).

Term comparisons are NOT case-sensitive.

The TextSimilarity uses extension methods that can be imported from the extensions library.

TextAnalyzer

The TextAnalyzer interface exposes language-specific properties and methods used in text analysis:

characterFilter is a function that manipulates text prior to stemming and tokenization;
termFilter is a filter function that returns a collection of terms from a term. It returns an empty collection if the term is to be excluded from analysis or, returns multiple terms if the term is split (at hyphens) and / or, returns modified term(s), such as applying a stemmer algorithm;
termSplitter returns a list of terms from text;
sentenceSplitter splits text into a list of sentences at sentence and line endings;
paragraphSplitter splits text into a list of paragraphs at line endings;
stemmer is a language-specific function that returns the stem of a term;
lemmatizer is a language-specific function that returns the lemma of a term;
termExceptions is a hashmap of words to token terms for special words that should not be re-capitalized, stemmed or lemmatized;
stopWords are terms that commonly occur in a language and that do not add material value to the analysis of text; and
syllableCounter returns the number of syllables in a word or text.

The English implementation of TextAnalyzer is included in this library.

TextTokenizer

The TextTokenizer extracts tokens from text for use in full-text search queries and indexes. It uses a TextAnalyzer and token filter in the tokenize and tokenizeJson methods that return a list of tokens from text or a document.

An unnamed factory constructor hydrates an implementation class.

TextDocument #

The TextDocument object model enumerates a text document's paragraphs, sentences, terms and tokens and provides functions that return text analysis measures:

averageSentenceLength is the average number of words in sentences;
averageSyllableCount is the average number of syllables per word in terms;
wordCount the total number of words in the sourceText;
fleschReadingEaseScore is a readibility measure calculated from sentence length and word length on a 100-point scale. The higher the score, the easier it is to understand the document;
fleschKincaidGradeLevel is a readibility measure relative to U.S. school grade level. It is also calculated from sentence length and word length .

Definitions #

corpus- the collection of documents for which an index is maintained.
character filter - filters characters from text in preparation of tokenization.
Damerau–Levenshtein distance - a metric for measuring the edit distance between two terms by counting the minimum number of operations (insertions, deletions or substitutions of a single character, or transposition of two adjacent characters) required to change one term into the other.
dictionary - is a hash of terms (vocabulary) to the frequency of occurence in the corpus documents.
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.
document frequency (dFt) - the number of documents in the corpus that contain a term.
edit distance - a measure of how dissimilar two terms are by counting the minimum number of operations required to transform one string into the other (Wikipedia (7)).

Flesch reading ease score - a readibility measure calculated from sentence length and word length on a 100-point scale. The higher the score, the easier it is to understand the document (Wikipedia(6)).
Flesch-Kincaid grade level - a readibility measure relative to U.S. school grade level. It is also calculated from sentence length and word length (Wikipedia(6)).

index - an inverted index used to look up document references from the corpus against a vocabulary of terms.
index-elimination - selecting a subset of the entries in an index where the term is in the collection of terms in a search phrase.
inverse document frequency (iDft) - is a normalized measure of how rare a term is in the corpus. It is defined as log (N / dft), where N is the total number of terms in the index. The iDft of a rare term is high, whereas the iDft of a frequent term is likely to be low.
Jaccard index measures similarity between finite sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets (from Wikipedia).
JSON is an acronym for "Java Script Object Notation", a common format for persisting data.
k-gram - a sequence of (any) k consecutive characters from a term. A k-gram can start with "$", denoting the start of the term, and end with "$", denoting the end of the term. The 3-grams for "castle" are { $ca, cas, ast, stl, tle, le$ }.
lemmatizer - lemmatisation (or lemmatization) in linguistics is the process of grouping together the inflected forms of a word so they can be analysed as a single item, identified by the word's lemma, or dictionary form (from Wikipedia).
Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data (from Wikipedia).
postings - a separate index that records which documents the vocabulary occurs in. In a positional index, the postings also records the positions of each term in the text 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. In a zoned index, the postings lists records the positions of each term in the text a zone.
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.
term filter - filters unwanted terms from a collection of terms (e.g. stopwords), breaks compound terms into separate terms and / or manipulates terms by invoking a stemmer and / or lemmatizer.
stemmer - stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form—generally a written word form (from Wikipedia).
stopwords - common words in a language that are excluded from indexing.
term frequency (Ft) is the frequency of a term in an index or indexed object.
term position is the zero-based index of a term in an ordered array of terms tokenized from the corpus.
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) (term position) in the text or frequency of occurrence (term frequency).
token filter - returns a subset of tokens from the tokenizer output.
tokenizer - a function that returns a collection of tokens from text, after applying a character filter, term filter, stemmer and / or lemmatizer.
vocabulary - the collection of terms indexed from the corpus.
zone is the field or zone of a document that a term occurs in, used for parametric indexes or where scoring and ranking of search results attribute a higher score to documents that contain a term in a specific zone (e.g. the title rather that the body of a document).

References #

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.

text_analysis 0.14.0
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Metadata

Tokenize text, compute document readbility and compare terms. #

Overview #

Tokenization

Readibility #

String Comparison #

Usage #

API #

TermSimilarity

TextAnalyzer

TextTokenizer

TextDocument #

Definitions #

References #

Issues #

← Metadata

Publisher

Metadata

License

Dependencies

More

text_analysis 0.14.0 text_analysis: ^0.14.0 copied to clipboard

Metadata

Tokenize text, compute document readbility and compare terms. #

Overview #

Tokenization

Readibility #

String Comparison #

Usage #

API #

TermSimilarity

TextAnalyzer

TextTokenizer

TextDocument #

Definitions #

References #

Issues #

← Metadata

Publisher

Metadata

License

Dependencies

More

text_analysis 0.14.0
text_analysis: ^0.14.0 copied to clipboard