Information Retrieval System

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Data Preprocessing
• Evaluation
• Contributing
• License
• Acknowledgements

Introduction

The Information Retrieval System is designed to efficiently retrieve relevant answers to user queries from a dataset of questions and answers. This project implements various text preprocessing techniques and utilizes TF-IDF vectorization for indexing and querying.

Features

• Text cleaning and normalization
• Tokenization and stopword removal
• Stemming and lemmatization
• Synonym expansion using WordNet
• TF-IDF vectorization for indexing
• Query processing and ranking using cosine similarity

Installation

1. Clone the repository: Download the project from GitHub.
2. Create and activate a virtual environment: Set up a virtual environment to manage dependencies.
3. Download NLTK data: Ensure that all required NLTK datasets are available for text processing.

Usage

1. Preprocess the data: Prepare the dataset by cleaning and normalizing the text.
2. Index the preprocessed data using TF-IDF: Create a TF-IDF matrix to index the preprocessed text.
3. Process and query the system: Implement the query system to process user queries and retrieve relevant answers based on the TF-IDF index.

Data Preprocessing

The preprocessing steps include:

• Cleaning: Removing punctuation, HTML tags, and brackets to reduce noise.
• Normalization: Converting text to lowercase, expanding abbreviations, and correcting spelling mistakes.
• Tokenization: Splitting text into individual words or tokens.
• Stopword Removal: Eliminating common words that do not contribute to the meaning.
• Stemming and Lemmatization: Reducing words to their base or root form.
• Synonym Expansion: Using WordNet to expand synonyms and enhance retrieval performance.

Evaluation

The performance of the Information Retrieval System is evaluated using metrics such as:

• Precision: The fraction of relevant instances among the retrieved instances.
• Recall: The fraction of relevant instances that have been retrieved over the total amount of relevant instances.
• F1-Score: The harmonic mean of precision and recall.
• Mean Average Precision (MAP): A measure of the quality of the retrieval process.

Acknowledgements

• NLTK
• scikit-learn
• Flask