Week 2: Basics of Text Preprocessing

Week 2: Basics of Text Preprocessing#

Overview#

This week, we’ll dive into the fundamental techniques of text preprocessing, a crucial step in any Natural Language Processing (NLP) pipeline. Text preprocessing is essential for cleaning and standardizing raw text data, making it suitable for further analysis and model training.

Learning Objectives#

By the end of this week, you will be able to:

  1. Understand the importance of text preprocessing in NLP tasks

  2. Implement and apply various tokenization techniques

  3. Perform text normalization, including case normalization and punctuation removal

  4. Identify and remove stop words from text data

  5. Use the NLTK (Natural Language Toolkit) library for text preprocessing tasks

Key Topics#

1. Tokenization#

  • Definition and importance of tokenization

  • Word tokenization vs. sentence tokenization

  • Challenges in tokenization (e.g., contractions, hyphenated words)

  • Different tokenization approaches (rule-based, statistical, neural)

2. Normalization#

  • Case normalization (lowercasing/uppercasing)

  • Punctuation removal

  • Handling special characters and numbers

  • Spelling correction and text canonicalization

3. Stop Word Removal#

  • Definition and purpose of stop words

  • Common stop words in English

  • Impact of stop word removal on NLP tasks

  • Considerations for domain-specific stop words

4. NLTK Library for Text Preprocessing#

  • Introduction to NLTK

  • Using NLTK for tokenization

  • NLTK’s built-in stop word lists

  • Additional NLTK preprocessing utilities

Practical Component#

In this week’s practical session, you will:

  • Install and set up the NLTK library

  • Implement a text preprocessing pipeline using NLTK

  • Experiment with different tokenization methods

  • Compare the effects of various preprocessing steps on sample texts

Assignment#

You will be given a dataset of raw text and tasked with creating a comprehensive preprocessing pipeline. Your solution should include tokenization, normalization, and stop word removal. You’ll also need to provide a brief report discussing the impact of each preprocessing step on the resulting text.

Looking Ahead#

The text preprocessing skills you learn this week will form the foundation for more advanced NLP tasks we’ll explore in the coming weeks. Next week, we’ll build upon these basics to delve into the fundamentals of language models.