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Challenge: Creating a Bag of Words | Basic Text Models
Introduction to NLP
course content

Conteúdo do Curso

Introduction to NLP

Introduction to NLP

1. Text Preprocessing Fundamentals
2. Stemming and Lemmatization
3. Basic Text Models
4. Word Embeddings

bookChallenge: Creating a Bag of Words

Tarefa

Your task is to display the vector for the 'graphic design' bigram in a BoW model:

  1. Import the CountVectorizer class to create a BoW model.
  2. Instantiate the CountVectorizer class as count_vectorizer, configuring it for a frequency-based model that includes both unigrams and bigrams.
  3. Utilize the appropriate method of count_vectorizer to generate a BoW matrix from the 'Document' column in the corpus.
  4. Convert bow_matrix to a dense array and create a DataFrame from it, setting the unique features (unigrams and bigrams) as its columns. Assign this to the variable bow_df.
  5. Display the vector for 'graphic design' as an array, rather than as a pandas Series.

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Seção 3. Capítulo 5
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bookChallenge: Creating a Bag of Words

Tarefa

Your task is to display the vector for the 'graphic design' bigram in a BoW model:

  1. Import the CountVectorizer class to create a BoW model.
  2. Instantiate the CountVectorizer class as count_vectorizer, configuring it for a frequency-based model that includes both unigrams and bigrams.
  3. Utilize the appropriate method of count_vectorizer to generate a BoW matrix from the 'Document' column in the corpus.
  4. Convert bow_matrix to a dense array and create a DataFrame from it, setting the unique features (unigrams and bigrams) as its columns. Assign this to the variable bow_df.
  5. Display the vector for 'graphic design' as an array, rather than as a pandas Series.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 3. Capítulo 5
toggle bottom row

bookChallenge: Creating a Bag of Words

Tarefa

Your task is to display the vector for the 'graphic design' bigram in a BoW model:

  1. Import the CountVectorizer class to create a BoW model.
  2. Instantiate the CountVectorizer class as count_vectorizer, configuring it for a frequency-based model that includes both unigrams and bigrams.
  3. Utilize the appropriate method of count_vectorizer to generate a BoW matrix from the 'Document' column in the corpus.
  4. Convert bow_matrix to a dense array and create a DataFrame from it, setting the unique features (unigrams and bigrams) as its columns. Assign this to the variable bow_df.
  5. Display the vector for 'graphic design' as an array, rather than as a pandas Series.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Tarefa

Your task is to display the vector for the 'graphic design' bigram in a BoW model:

  1. Import the CountVectorizer class to create a BoW model.
  2. Instantiate the CountVectorizer class as count_vectorizer, configuring it for a frequency-based model that includes both unigrams and bigrams.
  3. Utilize the appropriate method of count_vectorizer to generate a BoW matrix from the 'Document' column in the corpus.
  4. Convert bow_matrix to a dense array and create a DataFrame from it, setting the unique features (unigrams and bigrams) as its columns. Assign this to the variable bow_df.
  5. Display the vector for 'graphic design' as an array, rather than as a pandas Series.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 3. Capítulo 5
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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