A CV Parser Model using Entity Extraction Process and Big Data Tools

Das, Priyanka; Pandey, Manjusha; Rautaray, Siddharth Swarup

doi:10.5815/ijitcs.2018.09.03

Cited by 15 publications

(5 citation statements)

References 13 publications

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“…1. Selection -AI modernises and automates monotonous tasks in recruitment processes (Das et al, 2018). AI tools are being increasingly used in the selection process of candidates, which can help to speed up and make the processes more objective at an early stage in the first steps of the selection process (Allen et al, 2007).…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Attitude of Hungarian HR Professionals to Artificial Intelligence

Karácsony

2022

Naše Gospodarstvo/Our Economy

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Human resource (HR) management is one of an organisation’s most important core activities. As new technologies and software applications spread, it is important to recognise that human resource management must also mature and, to this end, must apply new technological guidelines. Artificial intelligence (AI) is one such promising technology trend that is likely to change the existing methods of HR management. This paper examines the attitudes that AI evokes among practicing HR professionals and assesses the potential for the practical application of these technologies. A survey, in the form of a questionnaire, was conducted among Hungarian HR managers, which allowed the collection of first-hand data. The survey was conducted in winter 2021 using the snowball method sampling procedure. The questionnaire mainly contained Likert-scale questions. The results of the research show that survey participants have mixed emotions about AI. The respondents largely agreed that the tools provided by AI are effective and their use helps HR management. The main limitation of the research is that it is limited to just one country, since the COVID-19 pandemic made it difficult to find and involve respondents in the research.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Attitude of Hungarian HR Professionals to Artificial Intelligence

Karácsony

2022

Naše Gospodarstvo/Our Economy

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“…Most appealingly, AI/ML applications offer efficiency by helping organizations sift through massive volumes of applicant data and consider larger applicant pools in a shorter time frame (e.g., Das, Pandey, & Rautaray, 2018). Even if AI/ML algorithms did not produce better employees, they lend potentially greater decision-making speed and efficiency than traditional assessments, saving organizations time and money.…”

Section: Organizational Benefits Of Ai/mlmentioning

confidence: 99%

“Where’s the I-O?” Artificial Intelligence and Machine Learning in Talent Management Systems

Gonzalez

Capman²,

Oswald

et al. 2019

PAD

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Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption by organizations seeking to identify and hire high-quality job applicants. Yet the volume, variety, and velocity of professional involvement among I-O psychologists remains relatively limited when it comes to developing and evaluating AI/ML applications for talent assessment and selection. Furthermore, there is a paucity of empirical research that investigates the reliability, validity, and fairness of AI/ML tools in organizational contexts. To stimulate future involvement and research, we share our review and perspective on the current state of AI/ML in talent assessment as well as its benefits and potential pitfalls; and in addressing the issue of fairness, we present experimental evidence regarding the potential for AI/ML to evoke adverse reactions from job applicants during selection procedures. We close by emphasizing increased collaboration among I-O psychologists, computer scientists, legal scholars, and members of other professional disciplines in developing, implementing, and evaluating AI/ML applications in organizational contexts.

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“…The third paper [3] introduces a novel approach to resume parsing and analysis, utilizing big data tools for entity extraction. The system employs NLP with R language for preprocessing, cleaning, tokenization, POS tagging, and transformation, emphasizing the use of Hadoop MapReduce for efficient processing of large datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

Resume Parser Using ML and NLP

2023

IRJMETS

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As the digitalization of recruitment processes continues to evolve, there is a growing need for advanced tools that can streamline the creation and optimization of resumes. This paper introduces a novel Resume Parser and Enhancement System (RPES) designed to revolutionize the way resumes are generated and refined. The proposed system utilizes state-of-the-art natural language processing (NLP) techniques, machine learning algorithms, and recommendation systems to automate the resume building process and provide personalized suggestions for improvement. The core functionality of the RPES involves efficient extraction of information from unstructured resumes, enabling seamless parsing of diverse document formats. Leveraging advanced NLP models, the system ensures accurate identification and categorization of key resume components, such as personal details, education, work experience, skills, and achievements. The parsed data serves as the foundation for the automatic generation of comprehensive and well-structured resumes. In addition to resume creation, the RPES incorporates an enhancement module that analyzes the parsed content to offer tailored recommendations for optimizing the overall quality and impact of the resume. The recommendation engine utilizes machine learning algorithms to identify areas of improvement, such as enhancing keyword relevance, suggesting additional skills, or providing guidance on formatting and phrasing. This iterative process aims to empower job seekers with dynamic tools to increase their chances of catching the attention of recruiters and applicant tracking systems. Furthermore, the RPES integrates a feedback loop mechanism, allowing users to provide real-time input and preferences to further personalize the resume-building and enhancement process. The system adapts and learns from user interactions, continuously improving its recommendations and adapting to evolving industry standards. In summary, this paper presents a comprehensive and intelligent Resume Parser and Enhancement System that goes beyond traditional parsing tools by combining advanced NLP, machine learning, and recommendation systems. The proposed system not only automates the resume creation process but also empowers users with personalized suggestions to optimize their resumes for enhanced visibility and success in the competitive job market.

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A CV Parser Model using Entity Extraction Process and Big Data Tools

Cited by 15 publications

References 13 publications

Analysis of the Attitude of Hungarian HR Professionals to Artificial Intelligence

Analysis of the Attitude of Hungarian HR Professionals to Artificial Intelligence

“Where’s the I-O?” Artificial Intelligence and Machine Learning in Talent Management Systems

Resume Parser Using ML and NLP

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