Session 2 — Applied NLP

This repository contains the materials for Session 2 of Applied NLP.

• Slides: see slides/ folder
• Notebooks: see notebooks/ folder

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📑 Session Outline

Topic: Phrases & Collocations

This session explores multi-word expressions and phrase patterns in literary texts. Using two works by the same author (as an example Alice's Adventures in Wonderland and Through the Looking-Glass), you'll learn to:

1. Bigrams & Trigrams (1_AppliedNLP_Session2_Bi_Trigrams.ipynb)

   • Compute frequent bigrams and trigrams across two texts
   • Compare phrase frequencies using normalized rates
   • Identify distinctive phrases for each work
   • Visualize most characteristic multi-word expressions

2. Pointwise Mutual Information (PMI) (2_AppliedNLP_Session2_PMI.ipynb)

   • Measure collocation strength using PMI scores
   • Identify strongly associated word pairs beyond raw frequency
   • Compare PMI-based collocations across works
   • Export reproducible analysis artifacts

3. POS Pattern Frequency (3_AppliedNLP_Session2_POS_Patterns.ipynb)

   • Analyze grammatical patterns in bigrams (ADJ+NOUN, VERB+NOUN, etc.)
   • Apply spaCy POS tagging for syntactic analysis
   • Visualize distribution of POS patterns
   • Compare syntactic phrase structure across texts

4. Phrase Diversity (n-gram TTR) (4_AppliedNLP_Session2_Phrase_Diversity.ipynb)

   • Measure phrase variety using Type-Token Ratio (TTR)
   • Compare lexical/phrasal diversity across works
   • Analyze diversity trends by section/chapter
   • Interpret TTR as indicator of stylistic formulaicity vs. variety

5. Collocation Network (5_AppliedNLP_Session2_Collocation_Network.ipynb)

   • Visualize word collocations as network graphs
   • Identify hub words and collocation clusters
   • Explore phrase structure through network topology
   • Compare collocation patterns between texts

Key Concepts

• N-grams: Contiguous sequences of n tokens (bigrams, trigrams)
• Collocations: Words that frequently co-occur in predictable patterns
• PMI: Statistical measure of association between words in bigrams
• TTR: Ratio of unique to total n-grams, measuring diversity
• POS patterns: Grammatical templates underlying phrases

Data

All notebooks work with Project Gutenberg texts in the data/ folder:

• Wonderland.txt — Alice's Adventures in Wonderland by Lewis Carroll
• Looking-Glass.txt — Through the Looking-Glass by Lewis Carroll

Notebooks include robust preprocessing to strip Gutenberg headers, normalize quotes, and handle encoding artifacts. For your texts, you should implement different preprocessing steps if needed.

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🚀 Environment Setup

Before starting, please fork this repository and create a fresh Python virtual environment.
All required libraries are listed in requirements.txt.

⚠️ If you encounter errors during pip install, try removing the version pinning for the failing package(s) in requirements.txt.
On Apple M1/M2 systems you may also need to install additional system packages (the “M1 shizzle”).

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macOS / Linux (bash/zsh)

# Select Python version (if using pyenv)
pyenv local 3.11.3

# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate

# Upgrade pip and install dependencies
pip install --upgrade pip
pip install -r requirements.txt

Windows (PowerShell)

# Select Python version (if using pyenv)
pyenv local 3.11.3

# Create and activate virtual environment
python -m venv .venv
.venv\Scripts\Activate.ps1

# Upgrade pip and install dependencies
python -m pip install --upgrade pip
pip install -r requirements.txt

Windows (Git Bash)

# Select Python version (if using pyenv)
pyenv local 3.11.3

# Create and activate virtual environment
python -m venv .venv
source .venv/Scripts/activate

# Upgrade pip and install dependencies
python -m pip install --upgrade pip
pip install -r requirements.txt

You’re now ready to run the session notebooks!

Deactivate the environment when you’re done:

deactivate