Viterbi Algorithm Visualiser for Part-of-Speech Tagging

Welcome to the Viterbi Algorithm Visualiser – your interactive guide to understanding the Viterbi algorithm for part-of-speech (POS) tagging! This tool is perfect for linguists, students, and developers who want to delve into sequence modeling and Hidden Markov Models (HMMs).

What is the Viterbi Algorithm Visualiser?

The Viterbi Algorithm Visualiser brings learning to life by letting you step through how the Viterbi algorithm works for POS tagging. With support for English, Swedish, and Korean, this tool demonstrates the algorithm’s effectiveness across diverse linguistic structures.

Features

• Interactive Visualisation: Observe the Viterbi algorithm in action as it computes the most probable sequence of POS tags for any given sentence.
• Multilingual Support: Switch between English, Swedish, or Korean datasets to explore how linguistic differences impact the algorithm.
• Accuracy Metrics:
  • English: 91.3%
  • Swedish: 90.2%
  • Korean: 79.2%
• Educational Focus: Ideal for anyone curious about HMMs and sequence tagging.

Get Started in 3 Steps

1️⃣ Clone the Repository

git clone https://github.com/emma-horton/viterbi-visualiser.git
cd viterbi-visualiser

2️⃣ Install Dependencies

Ensure you have Python installed, then run:

pip install -r requirements.txt

3️⃣ Launch the Application

Run the application locally:

python app.py

Open your browser and navigate to http://localhost:5001 to begin exploring!

How It Works

The visualiser employs the Viterbi algorithm, a dynamic programming technique that predicts the most probable POS tag sequence for a sentence. Key components include:

• Transition & Emission Probabilities: Generated from annotated datasets in English, Swedish, and Korean.
• Final Output: Presents the sentence annotated with predicted POS tags.

Future Improvements

The following enhancements are planned for future iterations of the visualiser:

• Dynamic Programming Table: Display step-by-step calculations of probabilities to improve user understanding.
• Enhanced User Interface: Introduce more intuitive controls and visual cues.
• Expanded Language Support: Add new languages with unique linguistic structures.