A system for detecting professional skills from resumes written in natural language

Chifu, Emil St.; Chifu, Viorica Rozina; Popa, I.; Salomie, Ioan

doi:10.1109/iccp.2017.8117003

Cited by 11 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chifu et al [6] have created skills, and web crawled resumes are checked for POS patterns after text preprocessing using the Stanford NLP framework. If words are not present in the skill ontology, new skills are updated for further skill detection using algorithms trained for specific lexical patterns.…”

Section: Named Entity Recognition For Semistructured Datamentioning

confidence: 99%

Semi-supervised deep learning based named entity recognition model to parse education section of resumes

Gaur

Saluja

Sivakumar

et al. 2020

Neural Comput & Applic

View full text Add to dashboard Cite

A job seeker’s resume contains several sections, including educational qualifications. Educational qualifications capture the knowledge and skills relevant to the job. Machine processing of the education sections of resumes has been a difficult task. In this paper, we attempt to identify educational institutions’ names and degrees from a resume’s education section. Usually, a significant amount of annotated data is required for neural network-based named entity recognition techniques. A semi-supervised approach is used to overcome the lack of large annotated data. We trained a deep neural network model on an initial (seed) set of resume education sections. This model is used to predict entities of unlabeled education sections and is rectified using a correction module. The education sections containing the rectified entities are augmented to the seed set. The updated seed set is used for retraining, leading to better accuracy than the previously trained model. This way, it can provide a high overall accuracy without the need of large annotated data. Our model has achieved an accuracy of 92.06% on the named entity recognition task.

show abstract

Section: Named Entity Recognition For Semistructured Datamentioning

confidence: 99%

Semi-supervised deep learning based named entity recognition model to parse education section of resumes

Gaur

Saluja

Sivakumar

et al. 2020

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…4) Automatic Slicing of CVs: Curriculum Vitae (CV) and resumes are structured documents that contain certain key elements information such as work experiences that talenthunt specialist usually look out for in CVs during a job advert placement. Emil St. et al [35] applied NLP text extraction techniques on several CV documents to determine candidate professional qualifications, which is useful for review and ease of vetting the relevance of the CV to the role advertised . At another instance, an NLP-based tool was designed to extract the logical sections of CVs using a set of CFG rules [28].…”

Section: B Related Workmentioning

confidence: 99%

Structural Vetting of Academic Proposals

Iwashokun¹,

Ade-Ibijola²

2022

IJACSA

View full text Add to dashboard Cite

Increasing postgraduate enrollments gives rise to many proposal documents required for vetting and human supervision. Reading and comprehension of large documents is a boring and somewhat difficult task for humans which can be delegated to machines. One way of assisting supervisors with this routine screening of academic proposals is to provide an artificial intelligent (AI) tool for initial structural vetting -checking if sections of proposals are complete and appear where they are supposed to. Natural Language Processing (NLP) techniques in AI for document vetting has been applied in legal and financial domains. However, in academia, available tools only perform tasks such as checking proposals for plagiarism, spellings or grammar, word editing, and not structural vetting of academic proposal. This paper presents a tool named Autoproofreader that attempts to perform the task of structural document review of proposals on behalf of the human expert using formal techniques and document structure understanding hinged on context free grammar rules (CFGs). The experimental results on a corpus of 20 academic proposals using confusion matrix technique for evaluation gives an overall of 87% accuracy. This tool is expected to be a useful aid in postgraduate supervision for vetting students' academic proposals.

show abstract

“…[41]. The literature on entity extraction approaches for natural language plain texts generally considers a subset of these specific features, but not all at once [6,14,9]. Besides, most plain texts written in natural language have organizational patterns.…”

Section: Introductionmentioning

confidence: 99%

DEEP, a methodology for entity extraction using organizational patterns: Application to job offers

Ramdani¹,

Brun²,

Bonjour³

et al. 2022

Knowledge-Based Systems

View full text Add to dashboard Cite

A system for detecting professional skills from resumes written in natural language

Cited by 11 publications

References 4 publications

Semi-supervised deep learning based named entity recognition model to parse education section of resumes

Semi-supervised deep learning based named entity recognition model to parse education section of resumes

Structural Vetting of Academic Proposals

DEEP, a methodology for entity extraction using organizational patterns: Application to job offers

Contact Info

Product

Resources

About