CV Extraction Methodology

Understanding the AI-powered CV parsing system and extraction algorithms

Two-Stage AI Pipeline

Our CV processing system uses a sophisticated two-stage approach to transform unstructured CV text into structured, AI-ready data.

1

CV Parsing & Extraction

Intelligent pattern recognition to extract structured information from CV text

  • Research areas identification
  • Publication extraction
  • Experience and leadership detection
  • Education and credentials parsing
→
2

AI Content Generation

Gemini AI creates personalized content using extracted CV context

  • Research narrative generation
  • Strategic alignment content
  • Personalized sabbatical planning
  • Context-aware responses

Stage 1: Enhanced CV Parsing System

The first stage processes raw CV text to extract meaningful, structured information using advanced pattern recognition and NLP techniques.

Extraction Methods

Research Areas

Identifies specific research fields using academic pattern recognition

Patterns: "research in", "expertise in", "specializing in"
Publications

Extracts publication titles, journals, and citation information

Patterns: Journal names, citation formats, publication years
Experience

Recognizes leadership roles, research positions, and achievements

Patterns: "Dean", "Professor", "Director", "Principal Investigator"
Education

Parses degrees, institutions, and academic credentials

Patterns: "Ph.D.", "M.Sc.", "B.Eng.", university names

Pattern Recognition & NLP

Our system uses sophisticated natural language processing to identify academic patterns and extract meaningful information.

Academic Pattern Examples

Research Areas
  • "research in [field]"
  • "expertise in [domain]"
  • "specializing in [area]"
  • "focus on [topic]"
Publications
  • "Journal of [Field]"
  • "[Year] [Author] [Title]"
  • "Conference on [Topic]"
  • "[Publisher] [Year]"
Leadership
  • "Dean of [Faculty]"
  • "Director of [Program]"
  • "Principal Investigator"
  • "Chair of [Department]"

Quality Assessment & Scoring

Our system provides comprehensive quality metrics to evaluate extraction effectiveness and guide improvements.

Quality Metrics

Research Areas Score

Measures the relevance and specificity of extracted research fields

Specific terms: +20 points Generic terms: -10 points Context relevance: +15 points
Publication Quality

Assesses the completeness and accuracy of publication extraction

Complete citations: +25 points Journal recognition: +15 points Fragment detection: -5 points
Experience Recognition

Evaluates leadership and achievement detection accuracy

Role identification: +20 points Achievement detection: +15 points Timeline accuracy: +10 points

User Configuration & Customization

Coming soon: Interactive configuration panel to customize extraction parameters and patterns.

Planned Configuration Options

Pattern Sensitivity

Adjust how strictly patterns must match for extraction

🔄 In Development
Custom Keywords

Add institution-specific research terms and patterns

🔄 In Development
Quality Thresholds

Set minimum confidence scores for data inclusion

🔄 In Development
Output Formatting

Customize data structure and export formats

🔄 In Development

Current System Configuration

Pattern Sensitivity: Medium (balanced accuracy/sensitivity)

Quality Threshold: 60% minimum confidence

Context Window: 100 characters around matches

Generic Term Filtering: Active (excludes "research", "study", etc.)