RESUME SCREENING AND JOB MATCHING SYSTEM USING NATURAL LANGUAGE PROCESSING

Author

S.Selsiya ,Dr. K. Merriliance

Abstract

 Resume screening is an important task in the recruitment process, but manually reviewing a large number of resumes is time-consuming and may lead to inconsistent evaluation. To address this problem, this paper proposes a Resume Screening and Job Matching System that automates the initial screening of candidate resumes based on job requirements. The proposed system is implemented as a web application using Python and Flask. Resume documents in PDF format are processed using Natural Language Processing techniques to extract and preprocess relevant textual information. Term Frequency-Inverse Document Frequency (TF-IDF) is used to represent the resume and job description as numerical vectors, while Cosine Similarity is used to measure the textual similarity between them. In addition, skill matching is performed to identify relevant and missing skills. A hybrid matching score is generated by combining textual similarity and skill matching, allowing candidates to be ranked according to their relevance to a particular job. The system also provides candidate application management and status tracking features. The proposed approach aims to reduce manual screening effort, improve consistency in initial candidate evaluation, and provide a simple and efficient solution for automated resume screening and job matching.

Keywords

Resume screening system, Automated job matching, • Information extraction (IE), AI-driven recruitment, Talent acquisition automation, Candidate ranking algorithm

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References

[1] Ramos, J. (2003). Using TF-IDF to Determine Word Relevance in Document Queries.

[2] Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.

[3] Jurafsky, D., & Martin, J. H. Speech and Language Processing. Pearson.

[4] Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

[5] Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media.

[6] Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT.

[7] Salton, G., & McGill, M. J. (1983). Introduction to Modern Information Retrieval. McGraw-Hill.

[8] Salton, G., Wong, A., & Yang, C. S. (1975). A Vector Space Model for Automatic Indexing. Communications of the ACM, 18(11), 613–620.

[9] Manning, C. D., Manning, C. D., & Raghavan, P. (2009). An Introduction to Information Retrieval. Cambridge University Press.

[10] Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781.

[11] Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. Proceedings of EMNLP, 1532–1543.

[12] Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of EMNLP-IJCNLP, 3982–3992.

[13] Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30.

[14] Lewis, M., Liu, Y., Goyal, N., et al. (2020). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. Proceedings of ACL, 7871–7880.

[15] McCallum, A. K., & Nigam, K. (1998). A Comparison of Event Models for Naive Bayes Text Classification. AAAI-98 Workshop on Learning for Text Categorization, 41–48.

[16] Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. Proceedings of ECML, 137–142.

[17] Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S. J., & McClosky, D. (2014). The Stanford CoreNLP Natural Language Processing Toolkit [17]. Proceedings of ACL System Demonstrations, 55–60.

[18] Loper, E., & Bird, S. (2002). NLTK: The Natural Language Toolkit. Proceedings of the ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, 63–70.

[19] Harris, Z. S. (1954). Distributional Structure. Word, 10(2–3), 146–162.

[20] Robertson, S., & Zaragoza, H. (2009). The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends in Information Retrieval, 3(4), 333–389.

 

 

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