Welcome to Textra-AI-Research’s documentation!

Table of contents:

• TEXTRA-IA Research
  • Context
  • Challenges We Have to Face
  • Objectives
  • Technological Framework
  • Future Directions
  • Our solution
• Pipeline
  • 1. Data Collection
  • 2. Document Analysis
  • 3. Knowledge Graph
  • 4. Synthesis Generation
  • 5. Trends Detection System
  • 6. User Interface
• ArXiv Papers Downloader
  • Dependencies
  • Class Implementation
  • Running the Downloader
  • Experimentation Tips
• PDF to Image Conversion
  • Requirements
  • Implementation
  • Directory Structure
  • Usage
• Tables and Figures Detection Using Layout Parser
  • Introduction
  • Installation
  • Components
  • Usage Examples
• Text Extraction
  • Process Overview
  • La Démarche (Approach)
  • Some Implementation Components
  • Configuration and Usage
  • Output Structure
• Preprocessing Data for Base Knowledge Graph
  • Initial Approach
  • Unicode and Text Cleaning
  • Improved Section Processing
  • Output Structure
• Reinforcement Learning Base Knowledge Graph
• Prerequisites
  • Software Requirements
  • Python Dependencies
  • Neo4j Setup
• Knowledge Graph Construction
  • Entity Extraction Process
  • Relationship Extraction Process
  • Knowledge Graph Building
• Knowledge Graph Enhancement
  • Enhancement Strategy
• Scientific Article Processing
  • Implementation Details
• Relationship Enhancement
  • Implementation Details
• Entity Consolidation
  • Implementation Details
  • Key Features
• Consolidated Graph Building
  • Implementation Details
  • Key Features
  • Usage Example
• Building and Exploring the Reinforcement Learning Knowledge Graph
  • Overview
  • Neo4j Knowledge Graph Interface: A Visual Guide
  • Domain and Field Exploration
  • Algorithm Improvements
  • More Knowledge Graph Node Types with examples
• Synthesis connected Pipeline
  • Overview
• Pipeline Components
  • Integration
  • Deployment
  • Usage
  • Customization