The Core NLP Pipeline

Our CV analyzer uses a pattern-based NLP approach that mimics human understanding through multiple processing layers:

CV Text → Text Extraction → Skill Pattern Matching → Experience Detection → Scoring & Classification

1. Text Extraction & Preprocessing

python

# Simple text extraction from files
def extract_text_from_file(file_path):
    # Reads raw text from .txt files
    # Converts everything to lowercase for consistent matching
    # Handles different encodings (UTF-8, Latin-1)

What it does: Takes messy, unstructured CV text and prepares it for analysis by standardizing the format.

2. Skill Detection – Pattern Matching Magic

python

def extract_skills(self, text):
    text_lower = text.lower()
    found_skills = {}
    
    for category, skills in self.skills_db.items():
        for skill in skills:
            # Uses word boundaries for exact matching
            if re.search(r'\b' + re.escape(skill) + r'\b', text_lower):
                found_skills[category].append(skill)

How it works:

• Word Boundary Detection: \bpython\b matches “python” but not “pythonic”
• Context-Aware Matching: Looks for skills in their natural context
• Multi-level Categorization: Groups skills into programming, databases, cloud, etc.

Example:

CV Text: "Experienced in Python and Django development"
→ Detects: "python" (programming), "django" (web_development)

3. Experience Extraction – Temporal Pattern Recognition

python

patterns = [
    r'(\d+)\s*years?\s*experience',
    r'experience\s*:\s*(\d+)\s*years?',
    r'(\d+)\s*years?\s*in\s*field'
]

NLP Techniques Used:

• Regular Expressions: Pattern matching for experience phrases
• Temporal Analysis: Identifying time-related information
• Context Understanding: Differentiating between “3 years experience” vs “3 years old”

4. Named Entity Recognition (Simplified)

python

def extract_candidate_name(self, text):
    lines = text.strip().split('\n')
    for line in lines[:3]:  # Check first 3 lines
        if line and len(line.split()) >= 2 and len(line) < 100:
            return line

What it does: Identifies candidate names by analyzing document structure and common naming patterns.

5. Semantic Understanding & Classification

Skill-Requirement Matching

python

# Calculate overlap between CV skills and job requirements
required_matches = len(set(required_skills) & set(all_cv_skills))

The NLP Logic:

• Set Operations: Mathematical intersection of required vs. found skills
• Weighted Scoring: Different importance for required vs. preferred skills
• Contextual Weighting: Experience contributes to overall score

Classification Algorithm

python

if match_result["total_score"] >= 70:
    status = "High Match"
elif match_result["total_score"] >= 50:
    status = "Medium Match"
else:
    status = "Low Match"

6. Advanced NLP Features in Action

Synonym & Variation Handling

The system naturally handles:

• “JavaScript” vs “JS” vs “javascript”
• “AWS” vs “Amazon Web Services”
• “Machine Learning” vs “ML”

Context Preservation

"I used Python for data analysis" → Skills: Python, Data Analysis
"Python certification completed" → Skills: Python

Pattern Resilience

Works with different CV formats:

• Bullet points: • Python, Java, SQL
• Sentences: "Proficient in Python and database management"
• Tables: Skills: Python | AWS | Docker

7. The Scoring Logic – How Decisions Are Made

python

required_score = (required_matches / total_required) * 60
preferred_score = (preferred_matches / total_preferred) * 30
experience_score = 10  # Based on meeting minimum requirements

total_score = required_score + preferred_score + experience_score

Why This Works:

• Required Skills (60%): Most important – must-have qualifications
• Preferred Skills (30%): Nice-to-have bonuses
• Experience (10%): Minimum threshold consideration

8. Batch Processing Intelligence

When processing multiple CVs, the system:

1. Parallel Analysis: Processes each CV independently
2. Comparative Ranking: Sorts candidates by match score
3. Pattern Aggregation: Identifies common skill gaps across candidates
4. Quality Control: Flags files with extraction issues

Real-World NLP Challenges Solved

Challenge	NLP Solution
Different CV formats	Pattern-based text extraction
Skill name variations	Word boundary matching
Experience quantification	Temporal pattern recognition
Missing information	Graceful degradation in scoring
Multiple languages	Unicode handling and encoding support

Why This Approach Becomes Traditional Methods

Traditional Approach:

• Manual reading → 30 minutes per CV
• Human bias and fatigue
• Inconsistent evaluation
• Missed patterns across multiple CVs

Our NLP Approach:

• Automated analysis → 30 seconds per CV
• Consistent, unbiased evaluation
• Pattern recognition across entire candidate pool
• Data-driven decision making

The Beauty of Pattern-Based NLP

Unlike complex machine learning models that require massive training data, our approach uses human-readable patterns that:

• Are transparent and explainable
• Don’t require training data
• Can be easily modified and extended
• Work reliably with small to large datasets
• Provide immediate results without model training

This makes our CV analyzer both powerful and accessible  delivering enterprise-level results with minimal infrastructure requirements!