Wikipedia-based extraction of key information from resumes

Ghufran, Mohammad; Bennacer, Nacéra; Quercini, Gianluca

doi:10.1109/rcis.2017.7956530

Cited by 2 publications

(2 citation statements)

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“…Wikipedia is the primary source of ontology, and the whole system is highly dependent on the same. Ghufran et al [11] leverage the fact that an individual's resume contents are available on Wikipedia for automatic annotation without POS tags. N-grams are constructed from keywords in a resume and then queried to Wikipedia.…”

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

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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.

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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

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“…Additionally, they did an experiment by using the Java programming language. Ghufran et al [11] used a dataset of 153 resumes written in English. They proposed an approach of five modules: the section delimiter module, the n-gram generator module, the n-gram interpretation graph constructor which is for each n-gram, and the interpretation filters module which is to get rid of some interpretations according to two criteria.…”

Section: Literature Reviewmentioning

confidence: 99%

Rule-Based Information Extraction from Multi-format Resumes for Automated Classification

Musleh

2024

MMEP

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Nowadays, with the expansion of the Internet, a lot of people publish their resumes on the internet and social media networks. Large companies receive hundreds of resumes per day, which comes in several formats such as Joint Photographic Experts Group (JPG), Portable Document Format (PDF) and Word files. Therefore, information extraction from resumes can be applied automatically by several methods. In this research, the important details that are taken from resumes are: name, date of birth, email, phone number, GPA, gender, nationality, and address. The private resumes dataset used is taken from different sources including open source as well as personally annotated. The processes of information extraction for resumes have been performed in different phases such as: pre-processing, converting the resumes files into PDF and information extraction by the rule-based method to extract the eight elements from resumes. To carry out the experiment, the Python language is used, particularly the spacy library and word2vec technique. Consequently, the experimental results demonstrate that the testing phase achieved 96.4% information extraction precision which is quite considerable in contrast to the techniques in the literature. The scheme is then extended to classify the resume based on the extracted information fields and exhibited classification accuracy, precision, recall and F1-score as 98.02%, 98.01%, 98% and 98%, respectively.

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Wikipedia-based extraction of key information from resumes

Cited by 2 publications

References 16 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

Rule-Based Information Extraction from Multi-format Resumes for Automated Classification

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