Rule-Based Information Extraction from Multi-format Resumes for Automated Classification
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".