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Deep learning based Resume Parser and Scoring

Data Science Asked on August 13, 2020

I want to know if Deep learning can be used for Resume Parsing and scoring of the resume.

Currently what I am doing is extracting the text from pdf or image using OCR/tesseract and finding the features like Email, Mobile No, Skills, Tenure, No of Companies, Awards etc from the text. So I have close to 100 features which are important for scoring the resume.

Can we do similar thing using Deep learning and will the accuracy be better ?
Any starting point/document/blog/github link which can help me get started on this.

I have gone through this link but this doesn’t not have code to start with.

One Answer

Resume parsing and scoring can be done by identifying relevant keyword in the text. NER (Named Entity Recognition) is a technique to identify relevant keywords in text. There are different framework available to work with NER and NLP as a whole. I will suggest you to look into spacy

to train a custom NER model.

Answered by Sanket Kumar Mali on August 13, 2020

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