Semantic Resume Parsing

Are you still bluntly adding keywords to your resume? That's a mistake! Ashby (one of the modern ATS platforms) introduced "AI-Assisted Application Review" in September 2024 (link in comments), which performs deeper semantic analysis of resumes. The system doesn't just look for specific words but searches for evidence of meeting criteria in context. How it works: ➡️ Recruiters set specific selection criteria in the job settings, which the AI algorithm uses to analyze resumes System algorithm: ➡️ When AI review is initiated, the system analyzes each resume looking for evidence of matching the specified criteria ➡️ AI determines if a candidate "Meets" or "Does Not Meet" each criterion ➡️ The system provides justification for its decision with specific quotes from the resume Matching determination mechanism: ➡️ AI parses resume content, including work experience, skills, and education ➡️ Compares found information with specified criteria ➡️ Provides the "best determination" based on analysis ➡️ For each decision, the system shows resume quotes serving as evidence Comprehensive semantic analysis: ➡️ The system analyzes not just the presence of keywords but their context in the resume ➡️ AI evaluates resumes in the context of achievements and actual experience ➡️ Comprehensive analysis distinguishes real skills from formal listing of required terms Candidate segmentation: ➡️ Recruiters can filter candidates by any combination of criteria ➡️ This allows reviewing candidates meeting all mandatory requirements first ➡️ Quickly checking and rejecting candidates who don't meet key criteria Human factor: ➡️ The final decision to advance or reject is made by a human recruiter ❗ ➡️ AI only helps structure the process and increase efficiency, not replace human evaluation Simply adding keywords without relevant experience won't improve a candidate's chances. ❗ Are you still listing keywords in your resume or have you started adding them contextually?

For all the progress in data science, one of the most stubborn problems that’s persisted has been resume parsing. Resume parsing can be complex — major variations in what titles/skills mean, discrepancies in file format, and jargon changing between industries. In fact, a recent study found that traditional ATS algorithms and rules-based parsers were only able to attain 60-70% accuracy, leading to talent mismatch, lost opportunities, and wasted effort. We put Refuel and an LLM based approach to the test, and realized higher accuracy (95% vs 60-70%), significant time/cost savings, and a flexible output schema. Here’s how we did it ⬇

𝐇𝐨𝐰 𝐀𝐈 𝐜𝐚𝐧 𝐬𝐢𝐦𝐩𝐥𝐢𝐟𝐲 𝐫𝐞𝐬𝐮𝐦𝐞 𝐬𝐜𝐫𝐞𝐞𝐧𝐢𝐧𝐠 𝐮𝐬𝐢𝐧𝐠 𝐍𝐋𝐏 🧑💻 Recently, I worked on a project: “𝑯𝒐𝒘 𝒄𝒂𝒏 𝑯𝑹 𝒕𝒆𝒂𝒎𝒔 𝒔𝒄𝒓𝒆𝒆𝒏 𝒉𝒖𝒏𝒅𝒓𝒆𝒅𝒔 𝒐𝒇 𝒓𝒆𝒔𝒖𝒎𝒆𝒔 𝒒𝒖𝒊𝒄𝒌𝒍𝒚 𝒂𝒏𝒅 𝒇𝒂𝒊𝒓𝒍𝒚?” Going through every resume manually takes hours — and sometimes good candidates are missed. So I built an AI-based Resume Screening System using NLP. Here’s how it works, step by step: 🔹 𝟏. 𝐄𝐱𝐭𝐫𝐚𝐜𝐭𝐬 𝐚𝐧𝐝 𝐩𝐚𝐫𝐬𝐞𝐬 𝐫𝐞𝐬𝐮𝐦𝐞𝐬 The system reads PDF/DOC files and extracts key information like skills, education, and experience. 🔹 𝟐. 𝐀𝐧𝐚𝐥𝐲𝐳𝐞𝐬 𝐭𝐡𝐞 𝐣𝐨𝐛 𝐝𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧 It understands the role requirements provided by the recruiter. 🔹 𝟑. 𝐂𝐨𝐦𝐩𝐚𝐫𝐞𝐬 𝐫𝐞𝐬𝐮𝐦𝐞𝐬 𝐮𝐬𝐢𝐧𝐠 𝐬𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐬𝐢𝐦𝐢𝐥𝐚𝐫𝐢𝐭𝐲 Using TF-IDF and BERT embeddings, it measures how closely a resume matches the job description — not just by keywords, but by context 🔹 𝟒. 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐬 𝐭𝐡𝐞 𝐜𝐚𝐧𝐝𝐢𝐝𝐚𝐭𝐞𝐬: ✅ Strong Fit 🟡 Moderate Fit ❌ Poor Fit 🔹 𝟓. (𝐎𝐩𝐭𝐢𝐨𝐧𝐚𝐥)– I built a Streamlit dashboard to visualize the results and highlight top candidates instantly. This tool can reduce resume screening time by 60–70%, bring more fairness to the process, and help HR focus on quality interviews instead of filtering. 💡 If you’re starting your AI/ML journey, try solving real-world problems like this. It’s the most effective and practical way to learn. Let me know if you’d like to explore this project or build something similar! #AI #NLP #ResumeScreening #MachineLearning #PythonProject #DataScienceForGood #HRTech #LearningByBuilding #AIProjects