How are Required and Preferred Skills, Qualification and Certification extracted by JD Parser?

RChilli's JD Parser (Job Description Parser) intelligently extracts structured data from unstructured job descriptions (JDs), including required and preferred skills, qualifications, and certifications. Here's a breakdown of how this is achieved:

Extraction Mechanism

1. AI-Powered Natural Language Processing

RChilli uses deep learning and AI frameworks to understand the semantic context of a job description. The parser analyzes the language and identifies patterns, keywords, and phrases that relate to:

• Mandatory (Required) Skills – These are identified through linguistic markers like "must have", "required", "mandatory", etc.

• Preferred Skills – Detected using softer indicators like "nice to have", "preferred", "would be a plus", etc.

• Qualifications – Educational requirements like degrees (e.g., B.Tech, MBA) are extracted based on context and mapped against a normalized education taxonomy.

• Certifications – Recognizes known certifications (e.g., PMP, AWS Certified) and links them to standardized certification terms using RChilli's Taxonomy 3.0.

2. Taxonomy & Ontology Support

RChilli’s Taxonomy 3.0 plays a crucial role by enriching the parser output. It includes:

• 3M+ skills

• 2.4M+ job profiles

• Multilingual support

This allows the parser to detect synonyms, abbreviations, and domain-specific terms. For example, "ML" is matched with "Machine Learning" and contextualized correctly.

3. Field Segregation and Categorization

Parsed JD data is returned in a structured JSON format with categorized sections, such as:

• RequiredSkills

• PreferredSkills

• Qualifications

• Certifications

These are clearly differentiated and can be independently accessed or used for filtering or matching purposes.

Customizability and Configuration

Dynamic API Settings

You can tailor extraction needs per API request using dynamic settings such as:

• reqskillsdrill (for detailed skill breakdown)

• reqsegregatedcertification (for structured certification data)

• reqeducationdrill (for detailed qualification fields)

These configurations allow users to control how deeply the parser extracts and categorizes data on-the-fly.

Key Benefits

• Precision: AI and taxonomy ensure context-based extraction.

• Flexibility: Dynamic settings provide granular control.

• Integration-Ready: JSON output is easy to consume via REST APIs.

• Bias-Free: Configurable to omit or highlight specific fields based on hiring preferences.

Further Reading & Documentation

• Job Parser Overview

• Dynamic API Settings Reference

• Taxonomy API Guide

If you still have a question regarding the plan, you can always contact RChilli Support via creating a ticket by sending an email at support@rchilli.com.