Automatic Candidates CV Segmentation Using Natural Language Processing |
Author(s): |
| Rajakumari H , RVS Technical campus, Coimbatore; Kiruthika P, RVS Technical campus, Coimbatore |
Keywords: |
| Information Retrieval, Natural Language Processing, Text Analysis, Recurrent Neural Networks, CV Parsing |
Abstract |
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In order to extract valuable information from multilingual, unstructured (free form) CV materials, this research suggests two NLP models. The model indicates the pertinent document sections (personal information, education, employment, etc.), as well as the pertinent specific information. (Names, addresses, roles, skill competencies, etc.) at the lowest hierarchy level. Our strategy makes use of the transformer architecture and the BERT language model, which serves as the encoder part's multilingual implementation. The models perform well on common accuracy tests after being trained and evaluated on a sizable, manually annotated CV dataset. By displaying the model attention and its vector representations, it was possible to examine the suggested models' significant end-to-end training and interpretability characteristics. |
Other Details |
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Paper ID: IJSRDV11I20231 Published in: Volume : 11, Issue : 2 Publication Date: 01/05/2023 Page(s): 346-352 |
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