removeEmptyDocuments

Remove empty documents from tokenized document array, bag-of-words model, or bag-of-n-grams model

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Syntax

newDocuments = removeEmptyDocuments(documents)
newBag = removeEmptyDocuments(bag)
[___,idx] = removeEmptyDocuments(___)

Description

example

newDocuments = removeEmptyDocuments(documents) removes documents which have no words from documents.

example

newBag = removeEmptyDocuments(bag) removes documents which have no words or n-grams from the bag-of-words or bag-of-n-grams model bag.

example

[___,idx] = removeEmptyDocuments(___) also returns the indices of the removed documents.

Examples

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Open Live Script

Remove documents containing no words from an array of tokenized documents.

Create an array of tokenized documents which includes empty documents.

documents = tokenizedDocument([
    "an example of a short sentence"
    ""
    "a second short sentence"
    ""])
documents = 
  4x1 tokenizedDocument:

    6 tokens: an example of a short sentence
    0 tokens:
    4 tokens: a second short sentence
    0 tokens:

Remove the empty documents.

newDocuments = removeEmptyDocuments(documents)
newDocuments = 
  2x1 tokenizedDocument:

    6 tokens: an example of a short sentence
    4 tokens: a second short sentence
Open Live Script

Remove documents containing no words from bag-of-words model.

Create a bag-of-words model from an array of tokenized documents.

documents = tokenizedDocument([
    "An example of a short sentence."
    ""
    "A second short sentence."
    ""]);
bag = bagOfWords(documents)
bag = 
  bagOfWords with properties:

          Counts: [4x9 double]
      Vocabulary: [1x9 string]
        NumWords: 9
    NumDocuments: 4

Remove the empty documents from the bag-of-words model.

newBag = removeEmptyDocuments(bag)
newBag = 
  bagOfWords with properties:

          Counts: [2x9 double]
      Vocabulary: [1x9 string]
        NumWords: 9
    NumDocuments: 2
Open Live Script

Remove documents containing no words from an array and use the indices of removed documents to remove the corresponding labels also.

Create an array of tokenized documents which includes empty documents.

documents = tokenizedDocument([
    "an example of a short sentence"
    ""
    "a second short sentence"
    ""])
documents = 
  4x1 tokenizedDocument:

    6 tokens: an example of a short sentence
    0 tokens:
    4 tokens: a second short sentence
    0 tokens:

Create a vector of labels.

labels = ["T"; "F"; "F"; "T"]
labels = 4x1 string
    "T"
    "F"
    "F"
    "T"

Remove the empty documents and get the indices of the removed documents.

[newDocuments, idx] = removeEmptyDocuments(documents)
newDocuments = 
  2x1 tokenizedDocument:

    6 tokens: an example of a short sentence
    4 tokens: a second short sentence


idx = 2×1

     2
     4

Remove the corresponding labels from labels.

labels(idx) = []
labels = 2x1 string
    "T"
    "F"

Input Arguments

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Input documents, specified as a tokenizedDocument array.

Input bag-of-words or bag-of-n-grams model, specified as a bagOfWords object or a bagOfNgrams object.

Output Arguments

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Output documents, returned as a tokenizedDocument array.

Output model, returned as a bagOfWords object or a bagOfNgrams object. The type of newBag is the same as the type of bag.

Indices of removed documents, returned as a vector of positive integers.

See Also

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Topics

Introduced in R2017b

Text Analytics Toolbox Documentation

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