NLP Chatbot with Text Summarization

Project Description

This project implements a simple chatbot using Natural Language Processing (NLP) techniques. The chatbot is built from scratch in Python, utilizing the NLTK (Natural Language Toolkit) and spaCy libraries. It features basic conversational abilities and a text summarization function.

Purpose

The main purposes of this project are:

1. To demonstrate the application of NLP techniques in building a chatbot
2. To provide a learning resource for understanding basic concepts of NLP
3. To showcase the implementation of a text summarization feature within a chatbot

Features

• Basic conversational abilities using keyword matching
• Response generation using TF-IDF (Term Frequency-Inverse Document Frequency) and cosine similarity
• Text summarization functionality using spaCy
• Simple and easy-to-understand Python implementation

Technologies Used

• Python
• NLTK (Natural Language Toolkit)
• spaCy
• scikit-learn

How It Works

1. The chatbot uses a predefined corpus (Wikipedia page on chatbots) as its knowledge base.
2. User input is processed and tokenized.
3. The bot generates responses using TF-IDF vectorization and cosine similarity.
4. A special command triggers the text summarization feature.

Usage

1. Run the script to start the chatbot.
2. Interact with the bot by typing your messages.
3. Use the command "~summarize" to trigger the text summarization feature.
4. Type "Bye" to end the conversation.

Future Improvements

• Expand the knowledge base
• Implement more advanced NLP techniques
• Add machine learning capabilities for improved response generation

License

This project is open source and available under the MIT License.