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Course Content

Introduction to NLP

1. Text Preprocessing Fundamentals

NLP and Its ApplicationsUnderstanding Text PreprocessingTokenizationTokenize a Sentence Tokenization Using Regular ExpressionsTokenize Using RegexRemoving Stop WordsApplying Text Preprocessing in Practice

2. Stemming and Lemmatization

StemmingStem the TokensLemmatizationPart of Speech TaggingLemmatization with POS Tagging

3. Basic Text Models

Overview of Vector Space ModelsKey Types of Vector Space ModelsBag of WordsCustomizing Bag of WordsCreate a Bag of WordsTF-IDFImplementing TF-IDF

4. Word Embeddings

Word Embeddings BasicsCBoW and Skip-gram Models (Optional)Implementing Word2VecCreate Word Embeddings

Introduction to NLP

Create Word EmbeddingsCreate Word Embeddings

Task

Now, it's time for you to train a Word2Vec model to generate word embeddings for the given corpus:

  1. Import the class for creating a Word2Vec model.
  2. Tokenize each sentence in the 'Document' column of the corpus by splitting each sentence into words separated by whitespaces. Store the result in the sentences variable.
  3. Initialize the Word2Vec model by passing sentences as the first argument and setting the following values as keyword arguments, in this order:
    • embedding size: 50;
    • context window size: 2;
    • minimal frequency of words to include in the model: 1;
    • model: skip-gram.
  4. Print the top-3 most similar words to the word 'bowl'.

Everything was clear?

Section 4. Chapter 4
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course content

Course Content

Introduction to NLP

1. Text Preprocessing Fundamentals

NLP and Its ApplicationsUnderstanding Text PreprocessingTokenizationTokenize a Sentence Tokenization Using Regular ExpressionsTokenize Using RegexRemoving Stop WordsApplying Text Preprocessing in Practice

2. Stemming and Lemmatization

StemmingStem the TokensLemmatizationPart of Speech TaggingLemmatization with POS Tagging

3. Basic Text Models

Overview of Vector Space ModelsKey Types of Vector Space ModelsBag of WordsCustomizing Bag of WordsCreate a Bag of WordsTF-IDFImplementing TF-IDF

4. Word Embeddings

Word Embeddings BasicsCBoW and Skip-gram Models (Optional)Implementing Word2VecCreate Word Embeddings

Introduction to NLP

Create Word EmbeddingsCreate Word Embeddings

Task

Now, it's time for you to train a Word2Vec model to generate word embeddings for the given corpus:

  1. Import the class for creating a Word2Vec model.
  2. Tokenize each sentence in the 'Document' column of the corpus by splitting each sentence into words separated by whitespaces. Store the result in the sentences variable.
  3. Initialize the Word2Vec model by passing sentences as the first argument and setting the following values as keyword arguments, in this order:
    • embedding size: 50;
    • context window size: 2;
    • minimal frequency of words to include in the model: 1;
    • model: skip-gram.
  4. Print the top-3 most similar words to the word 'bowl'.

Everything was clear?

Section 4. Chapter 4
toggle bottom row
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