removeEmptyDocuments

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

Syntax

newDocuments = removeEmptyDocuments(documents)

newBag = removeEmptyDocuments(bag)

[___,idx] = removeEmptyDocuments(___)

Description

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

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

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

Examples

Remove Empty Documents from Array

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 = 
  4×1 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 = 
  2×1 tokenizedDocument:

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

Remove Empty Documents from Bag-of-Words Model

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:

        NumWords: 9
          Counts: [4×9 double]
      Vocabulary: ["An"    "example"    "of"    "a"    "short"    "sentence"    "."    "A"    "second"]
    NumDocuments: 4

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

newBag = removeEmptyDocuments(bag)

newBag = 
  bagOfWords with properties:

        NumWords: 9
          Counts: [2×9 double]
      Vocabulary: ["An"    "example"    "of"    "a"    "short"    "sentence"    "."    "A"    "second"]
    NumDocuments: 2

Remove Documents and Corresponding Labels

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 = 
  4×1 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 = 4×1 string
    "T"
    "F"
    "F"
    "T"

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

[newDocuments, idx] = removeEmptyDocuments(documents)

newDocuments = 
  2×1 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 = 2×1 string
    "T"
    "F"

Input Arguments

documents — Input documents

tokenizedDocument array

Input documents, specified as a tokenizedDocument array.

bag — Input bag-of-words or bag-of-n-grams model

bagOfWords object | bagOfNgrams object

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

Output Arguments

newDocuments — Output documents

tokenizedDocument array

Output documents, returned as a tokenizedDocument array.

newBag — Output model

bagOfWords object | bagOfNgrams object

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

idx — Indices of removed documents

vector of positive integers

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

Version History

Introduced in R2017b