N-Gram Feature Based Resume Classification Using Machine Learning

Roy, Pradeep Kumar; Chahar, Shivam

doi:10.1007/978-3-031-10766-5_18

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…In addition to these pre-processing steps, we also introduce regular expressions and the spaCy library for extracting names and performing entity recognition, respectively, which enhance the accuracy and efficiency of the approach. Regular expressions are used to extract candidate names from the resume, which is critical in identifying and matching the candidate's experience https://www.indjst.org/ (11) This paper presents a machine learning-based automated resume classification model that helps classify resumes into different categories. The random forest classifier achieved high precision, recall, F1score, and accuracy for resume classification.…”

Section: Methodsmentioning

confidence: 99%

Automated Resume Parsing and Job Domain Prediction using Machine Learning

Sinha¹,

Akhtar²,

Kumar³

2023

IJST

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Objectives: This study aims to develop an efficient approach for parsing resumes and predicting job domains using natural language processing (NLP) techniques and named entity recognition to enhance the resume screening process for recruiters. Methods: The proposed approach involves preprocessing steps, such as cleaning, tokenization, stop-word removal, stemming, and lemmatization, implemented with the PyMuPDF and doc2text Python modules. Regular expressions and the spaCy library are utilized for entity recognition and name extraction. The model achieved a prediction accuracy of 92.08% and an F1-score of 0.92 on a dataset of 1000 resumes. An ablation experiment assessed the contributions of different factors. Findings:The approach demonstrated a high prediction accuracy of 92.08% and F1-score of 0.92 for job domain prediction, effectively identifying relevant job domains from resumes. Evaluations on individual job domains showed excellent precision and recall scores, validating its applicability. Preprocessing techniques significantly improved accuracy, while the integration of regular expressions and spaCy enhanced the model's performance. This approach automates resume screening, reducing recruiters' workload, saving time and effort, and improving candidate selection and the hiring process. Novelty: This study introduces a novel approach combining NLP techniques, regular expressions, and entity recognition for resume parsing and job domain prediction. This integration enhances accuracy and efficiency, offering a unique solution for resume screening.

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Section: Methodsmentioning

confidence: 99%

Automated Resume Parsing and Job Domain Prediction using Machine Learning

Sinha¹,

Akhtar²,

Kumar³

2023

IJST

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“…Use of N-grams was found to boost the accuracy of text classification in [19]. The authors in [20] found that the combination of unigrams with bigrams achieved the best performance using the random forest classifier. The BoW approach, however, does not capture the semantic similarity between keywords among resume documents since it is based on the morphological form of the word and not on its meaning.…”

Section: Related Workmentioning

confidence: 99%

Uniqueness meets Semantics: A Novel Semantically Meaningful Bag-of-Words Approach for Matching Resumes to Job Profiles

Susan,

Sharma,

Choudhary

2024

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In an increasingly competitive world, automated screening of resumes of candidates is the need of the hour given the large numbers of such resumes in career portals on the World Wide Web. Resume classification is a subset of the document classification problem in which the keywords extracted from the resume play a significant role in determining the job profile. In this paper, we explore the novel combination of uniqueness in terms of the number of occurrences of a keyword in a resume class as compared to the other resume classes, and the concept of semantics by representing the filtered keywords using word embeddings that can be used to find semantic similarities between resume documents. The principle of maximum entropy partitioning is used to find the keywords unique to a particular class. The aim is to use semantic representations of only those keywords that occur more frequently in one class more than in any other class; these are then passed as input to a Bidirectional long short-term memory (LSTM) for classification. Our experiments on a benchmark dataset proves that the proposed approach outperforms the state of the art in text classification by a significant margin proving the efficacy of our approach.

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N-Gram Feature Based Resume Classification Using Machine Learning

Cited by 2 publications

References 28 publications

Automated Resume Parsing and Job Domain Prediction using Machine Learning

Automated Resume Parsing and Job Domain Prediction using Machine Learning

Uniqueness meets Semantics: A Novel Semantically Meaningful Bag-of-Words Approach for Matching Resumes to Job Profiles

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