This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

removeWords

Remove selected words from documents or bag-of-words model

Syntax

newDocuments = removeWords(documents,words)
newDocuments = removeWords(documents,idx)
newBag = removeWords(bag,words)
newBag = removeWords(bag,idx)

Description

example

newDocuments = removeWords(documents,words) removes the specified words from documents.

example

newDocuments = removeWords(documents,idx) removes words by specifying the numeric or logical indices idx of the words in documents.Vocabulary. This syntax is the same as newDocuments = removeWords(documents,documents.Vocabulary(idx)).

example

newBag = removeWords(bag,words) removes the specified words from the bag-of-words model bag.

example

newBag = removeWords(bag,idx) removes words by specifying the numeric or logical indices idx of the words in bag.Vocabulary. This syntax is the same as newBag = removeWords(bag,bag.Vocabulary(idx)).

Examples

collapse all

Remove words from an array of documents by inputting a string array of words to removeWords.

Create an array of tokenized documents.

documents = tokenizedDocument([
    "an example of a short sentence" 
    "a second short sentence"]);

Remove the words "short" and "second".

words = ["short" "second"];
newDocuments = removeWords(documents,words)
newDocuments = 
  2x1 tokenizedDocument:

    5 tokens: an example of a sentence
    2 tokens: a sentence

To remove the default list of stop words using the language details of documents, use removeStopWords.

To remove a custom list of stop words, use the removeWords function. You can use the stop word list returned by the stopWords function as a starting point.

Load the example data. The file sonnetsPreprocessed.txt contains preprocessed versions of Shakespeare's sonnets. The file contains one sonnet per line, with words separated by a space. Extract the text from sonnetsPreprocessed.txt, split the text into documents at newline characters, and then tokenize the documents.

filename = "sonnetsPreprocessed.txt";
str = extractFileText(filename);
textData = split(str,newline);
documents = tokenizedDocument(textData);

View the first few documents.

documents(1:5)
ans = 
  5x1 tokenizedDocument:

    70 tokens: fairest creatures desire increase thereby beautys rose might never die riper time decease tender heir might bear memory thou contracted thine own bright eyes feedst thy lights flame selfsubstantial fuel making famine abundance lies thy self thy foe thy sweet self cruel thou art worlds fresh ornament herald gaudy spring thine own bud buriest thy content tender churl makst waste niggarding pity world else glutton eat worlds due grave thee
    71 tokens: forty winters shall besiege thy brow dig deep trenches thy beautys field thy youths proud livery gazed tatterd weed small worth held asked thy beauty lies treasure thy lusty days say thine own deep sunken eyes alleating shame thriftless praise praise deservd thy beautys thou couldst answer fair child mine shall sum count make old excuse proving beauty succession thine new made thou art old thy blood warm thou feelst cold
    65 tokens: look thy glass tell face thou viewest time face form another whose fresh repair thou renewest thou dost beguile world unbless mother fair whose uneard womb disdains tillage thy husbandry fond tomb selflove stop posterity thou art thy mothers glass thee calls back lovely april prime thou windows thine age shalt despite wrinkles thy golden time thou live rememberd die single thine image dies thee
    71 tokens: unthrifty loveliness why dost thou spend upon thy self thy beautys legacy natures bequest gives nothing doth lend frank lends free beauteous niggard why dost thou abuse bounteous largess thee give profitless usurer why dost thou great sum sums yet canst live traffic thy self alone thou thy self thy sweet self dost deceive nature calls thee gone acceptable audit canst thou leave thy unused beauty tombed thee lives th executor
    61 tokens: hours gentle work frame lovely gaze every eye doth dwell play tyrants same unfair fairly doth excel neverresting time leads summer hideous winter confounds sap checked frost lusty leaves quite gone beauty oersnowed bareness every summers distillation left liquid prisoner pent walls glass beautys effect beauty bereft nor nor remembrance flowers distilld though winter meet leese show substance still lives sweet

Create a list of stop words starting with the output of the stopWords function.

customStopWords = [stopWords "thy" "thee" "thou" "dost" "doth"];

Remove the custom stop words from the documents and view the first few documents.

documents = removeWords(documents,customStopWords);
documents(1:5)
ans = 
  5x1 tokenizedDocument:

    62 tokens: fairest creatures desire increase thereby beautys rose might never die riper time decease tender heir might bear memory contracted thine own bright eyes feedst lights flame selfsubstantial fuel making famine abundance lies self foe sweet self cruel art worlds fresh ornament herald gaudy spring thine own bud buriest content tender churl makst waste niggarding pity world else glutton eat worlds due grave
    61 tokens: forty winters shall besiege brow dig deep trenches beautys field youths proud livery gazed tatterd weed small worth held asked beauty lies treasure lusty days say thine own deep sunken eyes alleating shame thriftless praise praise deservd beautys couldst answer fair child mine shall sum count make old excuse proving beauty succession thine new made art old blood warm feelst cold
    52 tokens: look glass tell face viewest time face form another whose fresh repair renewest beguile world unbless mother fair whose uneard womb disdains tillage husbandry fond tomb selflove stop posterity art mothers glass calls back lovely april prime windows thine age shalt despite wrinkles golden time live rememberd die single thine image dies
    52 tokens: unthrifty loveliness why spend upon self beautys legacy natures bequest gives nothing lend frank lends free beauteous niggard why abuse bounteous largess give profitless usurer why great sum sums yet canst live traffic self alone self sweet self deceive nature calls gone acceptable audit canst leave unused beauty tombed lives th executor
    59 tokens: hours gentle work frame lovely gaze every eye dwell play tyrants same unfair fairly excel neverresting time leads summer hideous winter confounds sap checked frost lusty leaves quite gone beauty oersnowed bareness every summers distillation left liquid prisoner pent walls glass beautys effect beauty bereft nor nor remembrance flowers distilld though winter meet leese show substance still lives sweet

Remove words from documents by inputting a vector of numeric indices to removeWords.

Create an array of tokenized documents.

documents = tokenizedDocument([
    "I love MATLAB"
    "I love MathWorks"])
documents = 
  2x1 tokenizedDocument:

    3 tokens: I love MATLAB
    3 tokens: I love MathWorks

View the vocabulary of documents.

documents.Vocabulary
ans = 1x4 string array
    "I"    "love"    "MATLAB"    "MathWorks"

Remove the first and third words of the vocabulary from the documents by specifying the numeric indices [1 3].

idx = [1 3];
newDocuments = removeWords(documents,idx)
newDocuments = 
  2x1 tokenizedDocument:

    1 tokens: love
    2 tokens: love MathWorks

Alternatively, you can specify logical indices.

idx = logical([1 0 1 0]);
newDocuments = removeWords(documents,idx)
newDocuments = 
  2x1 tokenizedDocument:

    1 tokens: love
    2 tokens: love MathWorks

Remove the stop words from a bag-of-words model by inputting a list of stop words to removeWords. Stop words are words such as "a", "the", and "in" which are commonly removed from text before analysis.

documents = tokenizedDocument([
    "an example of a short sentence" 
    "a second short sentence"]);
bag = bagOfWords(documents);
newBag = removeWords(bag,stopWords)
newBag = 
  bagOfWords with properties:

          Counts: [2x4 double]
      Vocabulary: ["example"    "short"    "sentence"    "second"]
        NumWords: 4
    NumDocuments: 2

Remove words from a bag-of-words model by inputting a vector of numeric indices to removeWords.

Create an array of tokenized documents.

documents = tokenizedDocument([
    "I love MATLAB"
    "I love MathWorks"]);
bag = bagOfWords(documents)
bag = 
  bagOfWords with properties:

          Counts: [2x4 double]
      Vocabulary: ["I"    "love"    "MATLAB"    "MathWorks"]
        NumWords: 4
    NumDocuments: 2

View the vocabulary of bag.

bag.Vocabulary
ans = 1x4 string array
    "I"    "love"    "MATLAB"    "MathWorks"

Remove the first and third words of the vocabulary from the bag-of-words model by specifying the numeric indices [1 3].

idx = [1 3];
newBag = removeWords(bag,idx)
newBag = 
  bagOfWords with properties:

          Counts: [2x2 double]
      Vocabulary: ["love"    "MathWorks"]
        NumWords: 2
    NumDocuments: 2

Alternatively, you can specify logical indices.

idx = logical([1 0 1 0]);
newBag = removeWords(bag,idx)
newBag = 
  bagOfWords with properties:

          Counts: [2x2 double]
      Vocabulary: ["love"    "MathWorks"]
        NumWords: 2
    NumDocuments: 2

Input Arguments

collapse all

Input documents, specified as a tokenizedDocument array.

Input bag-of-words model, specified as a bagOfWords object.

Words to remove, specified as a string vector, character vector, or cell array of character vectors. If you specify words as a character vector, then the function treats it as a single word.

Data Types: string | char | cell

Indices of words to remove, specified as a vector of numeric indices or a vector of logical indices. The indices in idx correspond to the locations of the words in the Vocabulary property of the input documents or bag-of-words model.

Example: [1 5 10]

Output Arguments

collapse all

Output documents, returned as a tokenizedDocument array.

Output bag-of-words model, returned as a bagOfWords object.

Introduced in R2017b