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@article{200073,
        author = {Aayush Kholay and Rutuja Kshirsagar and Vedant Nandanwar and Vedant Sinare},
        title = {Beyond Keywords: Intelligent Resume Shortlisting Using Semantic Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {1163-1169},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200073},
        abstract = {This Keyword-based filtering is the most used method by Traditional ATS for resume filtering. This results in strict textual matching which keeps eligible applicants out. The proposed solution for overcoming this limitation is the Smart Resume Shortlisting Tool: an intelligent candidate screener which helps enhance recruitment efficiency without completely replacing ATS platforms. To determine the contextual meaning of job descriptions and resumes this system makes the use of semantic analysis. Sophisticated NLP models are used to represent candidate profiles and job requirements as semantic embeddings. These semantic embeddings capture skills, experience, and role similarities that are beyond precise keyword matching. As a relevance indicator JD Matching Percentage, and for weighted similarity scores across multiple dimensions (skills, experience, and education) a candidate ranking module is applied. Additionally, the system offers side by side comparisons of candidates leading to a speeded process of informed decision making which ensures dynamic interpretation and context aware shortlisting against ATS. This model fits within the current hiring procedures and is an efficient way to assess the applicants altogether instead of the frequency of keywords.},
        keywords = {Candidate Screening, Semantic Analysis, NLP, Resume Ranking, Job Description Matching.},
        month = {May},
        }