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1 Texts, lists and words 2 Simple statistics 3 Text corpora 4 WordNet 5 Regular expressions 6 Algorithms and libraries 7 Categorization and tagging 8 Supervised classification 9 Information extraction 10 Context-free grammar
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Unit 2 Simple statistics

Learning outcomes

By the end of this unit you should:

  • apply statistical methods to analyze text data
  • create visualizations for text analysis results
  • calculate and interpret text complexity metrics
  • compare statistical measures across different texts
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Activity 1 Statistical concepts in NLP

Read about the role of statistics in natural language processing.

Statistics form the foundation of many NLP techniques. In this unit, we'll explore how descriptive statistics help us understand text patterns, measure text complexity, and compare different documents. Unlike traditional statistical analysis, text statistics must account for the unique properties of language data.

Key statistical concepts we'll cover:

  • Frequency distributions: How often words appear in text
  • Measures of central tendency: Average word length, sentence length
  • Measures of variability: Lexical diversity, vocabulary richness
  • Text complexity metrics: Readability scores and comprehension levels

Activity 2 Introduction to text metrics

Watch this video introducing fundamental text analysis metrics.

This video (8 minutes) demonstrates how to calculate basic text statistics using Python. You'll see examples of word frequency analysis, sentence length distribution, and lexical diversity measurements.

Activity 3 Interactive statistics calculator

Use the interactive tool to calculate text statistics.

Enter text in the field below to see real-time statistical analysis. The tool will calculate word count, character count, average word length, sentence count, and lexical diversity.

Text Statistics Calculator

Activity 4 Frequency distributions

Watch how to create and interpret frequency distributions.

This video (5 minutes) shows how to create frequency distributions from text data and visualize them using Python libraries like matplotlib and seaborn.

Activity 5 Coding exercise: Statistical functions

Implement statistical functions from scratch.

Complete the following Python functions to calculate text statistics. Don't use built-in libraries - implement the calculations yourself to understand the underlying concepts.

Code Challenge

Implement these functions:

  1. calculate_type_token_ratio(text) - Lexical diversity measure
  2. average_sentence_length(text) - Mean words per sentence
  3. flesch_reading_ease(text) - Readability score
  4. word_frequency_distribution(text) - Word frequency dictionary
def calculate_type_token_ratio(text):
    """Calculate the ratio of unique words to total words"""
    # Your code here
    pass

def average_sentence_length(text):
    """Calculate average number of words per sentence"""
    # Your code here
    pass

Activity 6 Data visualization dashboard

Create interactive visualizations for text analysis.

Build a comprehensive dashboard that displays multiple statistical views of text data. This activity combines statistical calculation with data visualization.

Text Analysis Dashboard

Activity 7 Comparative text analysis

Compare statistical measures across different text types.

Use the comparison tool to analyze different text types (academic writing, news articles, social media posts) and observe how statistical measures vary across genres.

Text Comparison Tool
Text A
Text B

Unit Review

Test your understanding of statistical concepts in NLP:

Self-Assessment Quiz

1. What does the type-token ratio measure?




2. Which metric is used to measure text readability?




3. A higher lexical diversity score indicates: