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Please enable JavaScript in your browser settings or update your browser.Understanding Text Preprocessing | Text Preprocessing Fundamentals
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

Understanding Text PreprocessingUnderstanding Text Preprocessing

The Need for Text Preprocessing

Before delving into the complexities of modeling and analysis in NLP, it's essential to understand the critical step that precedes these tasks: text preprocessing.

Text preprocessing is the foundational process of preparing raw text data into a clean, standardized form that can be used effectively by NLP models.

Raw text data is often messy and unstructured. It may contain errors, inconsistencies, slang, abbreviations, and various languages, making it challenging for NLP models to understand and process the text accurately.

Preprocessing transforms this raw text into a more manageable form, reducing noise and complexity, which enables models to perform tasks such as classification, sentiment analysis, and language translation more effectively.

Core Text Preprocessing Techniques

The text preprocessing phase encompasses several key techniques, each addressing different aspects of the text data:

  • tokenization;
  • cleaning and normalization;
  • stop words removal;
  • stemming and lemmatization;
  • part-of-speech tagging.
Don't worry if some terms are unfamiliar to you, we'll cover each of these techniques in the following chapters.

Why NLTK?

The NLTK (Natural Language Toolkit) library is a Python library for NLP which we will actively use in our course for text preprocessing. Its intuitive design and extensive documentation cater to both beginners and experienced NLP practitioners, facilitating easy implementation of complex NLP operations.

Additionally, NLTK serves as a valuable educational resource with its rich collection of datasets and tutorials, supported by a large and active community that contributes to its continuous improvement.

Task

Your task is to import the nltk library without any aliases.

Everything was clear?

Section 1. Chapter 2
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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

Understanding Text PreprocessingUnderstanding Text Preprocessing

The Need for Text Preprocessing

Before delving into the complexities of modeling and analysis in NLP, it's essential to understand the critical step that precedes these tasks: text preprocessing.

Text preprocessing is the foundational process of preparing raw text data into a clean, standardized form that can be used effectively by NLP models.

Raw text data is often messy and unstructured. It may contain errors, inconsistencies, slang, abbreviations, and various languages, making it challenging for NLP models to understand and process the text accurately.

Preprocessing transforms this raw text into a more manageable form, reducing noise and complexity, which enables models to perform tasks such as classification, sentiment analysis, and language translation more effectively.

Core Text Preprocessing Techniques

The text preprocessing phase encompasses several key techniques, each addressing different aspects of the text data:

  • tokenization;
  • cleaning and normalization;
  • stop words removal;
  • stemming and lemmatization;
  • part-of-speech tagging.
Don't worry if some terms are unfamiliar to you, we'll cover each of these techniques in the following chapters.

Why NLTK?

The NLTK (Natural Language Toolkit) library is a Python library for NLP which we will actively use in our course for text preprocessing. Its intuitive design and extensive documentation cater to both beginners and experienced NLP practitioners, facilitating easy implementation of complex NLP operations.

Additionally, NLTK serves as a valuable educational resource with its rich collection of datasets and tutorials, supported by a large and active community that contributes to its continuous improvement.

Task

Your task is to import the nltk library without any aliases.

Everything was clear?

Section 1. Chapter 2
toggle bottom row
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