Resume Validation and Filtration using Natural Language Processing

Alamelu, M.; Kumar, Dikshit; Sanjana, R; Sree, J.Subha; Devi, A.Sangeerani; Kavitha, D.

doi:10.1109/iemecon53809.2021.9689075

Cited by 11 publications

(3 citation statements)

References 4 publications

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“…To enhance efficiency and fairness, various methods have been developed to optimize procedures such the holistic admission process [31] and information ordering [2], or automate particular tasks such as resume screening [47], assessment [34], and information extraction [30,35]. Natural Language Processing (NLP) [28] also has been used to detect and correct resume errors [41] and conduct rating classification while reducing human bias [3].…”

Section: Ai-enhanced Approaches For Materials Screening and Holistic ...mentioning

confidence: 99%

BiasEye: A Bias-Aware Real-time Interactive Material Screening System for Impartial Candidate Assessment

Liu,

Jiang,

Pan

et al. 2024

Proceedings of the 29th International Conference on Intelligent User Interfaces

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In the process of evaluating competencies for job or student recruitment through material screening, decision-makers can be influenced by inherent cognitive biases, such as the screening order or anchoring information, leading to inconsistent outcomes. To tackle this challenge, we conducted interviews with seven experts to understand their challenges and needs for support in the screening process. Building on their insights, we introduce BiasEye, a bias-aware real-time interactive material screening visualization system. BiasEye enhances awareness of cognitive biases by improving information accessibility and transparency. It also aids users in identifying and mitigating biases through a machine learning (ML) approach that models individual screening preferences. Findings from a mixed-design user study with 20 participants demonstrate that, compared to a baseline system lacking our bias-aware features, BiasEye increases participants' bias awareness and boosts their confidence in making final decisions. At last, we discuss the potential of ML and visualization in mitigating biases during human decision-making tasks. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Visualization; User studies.

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Section: Ai-enhanced Approaches For Materials Screening and Holistic ...mentioning

confidence: 99%

BiasEye: A Bias-Aware Real-time Interactive Material Screening System for Impartial Candidate Assessment

Liu,

Jiang,

Pan

et al. 2024

Proceedings of the 29th International Conference on Intelligent User Interfaces

View full text Add to dashboard Cite

show abstract

“…Impact: Job seekers are more likely to be matched with job postings that closely align with their qualifications, leading to more successful job applications. Data-Driven Decision-Making: Positive Effect: The framework uses data-driven models to make candidate selections, reducing the impact of bias and subjectivity [9]. Impact: Employers can make more objective and fair hiring decisions, promoting diversity and inclusivity in their workforce.…”

Section: Parser Systemmentioning

confidence: 99%

Resume Matching Framework via Ranking and Sorting Using NLP and Deep Learning

2023

IRJMETS

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In today's competitive job market, both job seekers and employers face the challenge of efficiently matching skills, qualifications, and job requirements. To address this issue, we propose a Resume Matching Framework that leverages Natural Language Processing (NLP) and Deep Learning techniques to rank and sort resumes based on their relevance to job postings. The framework begins by preprocessing both resumes and job postings, extracting key information, and transforming the text data into structured representations. We employ state-of-the-art NLP models, such as BERT and GPT-3, to capture the semantic meaning of the text, enabling a deeper understanding of job descriptions and candidate resumes. The core of the framework is a deep learning model designed for ranking and sorting. We train this model on a dataset consisting of labeled resume-job posting pairs, where each pair is assigned a relevance score. Our framework incorporates several innovative components, including Feature Extraction, Contextual Understanding, Ranking and Sorting, Scalability, and Customization. The proposed Resume Matching Framework offers significant advantages for both job seekers and employers. Job seekers can benefit from a more efficient job search process, as their resumes are more likely to be matched with relevant job opportunities. Employers can streamline the hiring process by quickly identifying the most suitable candidates for their job postings. We evaluate the framework's performance on a diverse dataset and demonstrate its effectiveness in improving the job-matching process.

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“…Although resumes generally adhere to a standard format, the specific skills and qualifications demanded by each role may necessitate varying levels of specificity in their creation [3]. The majority of contemporary job applications require electronic resumes.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Recommendations and Classification System for Unstructured Resume Documents

Channabasamma¹,

Suresh²

2023

RIA

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With the burgeoning growth of the job market and a surge in applications, the processes of job recommendation and candidate selection have become complex and labor-intensive. The advent of new technologies such as machine learning has automated these processes, yet the unstructured nature of resumes, often in PDF format, necessitates laborious data extraction for efficient skill-based candidate screening and categorization. Ineffectual recruitment can result from mismatched skills. The system proposed in this study aims to address these challenges by automatically fetching and categorizing resumes, extracting critical information, and utilizing job descriptions for candidate selection and recommendations. Unstructured data from PDF documents is extracted using a PDF reader, and machine learning algorithms, specifically logistic regression and Gaussian Naï ve Bayes, are employed for generating recommendations. In an innovative approach, this system not only classifies resumes but also recommends updates or rewrites. Performance of the proposed system is evaluated in terms of classification accuracy and the effectiveness of update recommendations, and results are compared with alternative models. This research represents a significant advancement in the application of machine learning to the automation of job recommendation and candidate selection processes.

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Resume Validation and Filtration using Natural Language Processing

Cited by 11 publications

References 4 publications

BiasEye: A Bias-Aware Real-time Interactive Material Screening System for Impartial Candidate Assessment

BiasEye: A Bias-Aware Real-time Interactive Material Screening System for Impartial Candidate Assessment

Resume Matching Framework via Ranking and Sorting Using NLP and Deep Learning

Machine Learning-Based Recommendations and Classification System for Unstructured Resume Documents

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