A resume is a brief summary of the skills and experiences. Companies' recruiters and HR teams have a tough time scanning thousands of qualified resumes. They either need many people to do this or miss out on qualified candidates.
Spending too many labor hours segregating candidates' resume's manually is a waste of a company's time, money, and productivity. Recruiters, therefore, use resume parsers in order to streamline the resume and applicant screening process.
Parsing technology allows recruiters to electronically gather, store, and organize large quantities of resumes. Once acquired, the resume data can be easily searched through and analyzed. Resumes are an ideal example of unstructured data. Since there is no widely accepted resume layout, each resume may have its own style of formatting, different text blocks and different category titles.
In this project we dive into building a parser tool using Python and basic natural language processing techniques. We would be using Python's libraries to implement various NLP (natural language processing) techniques like tokenization, lemmatization, parts of speech tagging, etc., for building a resume parser in Python.
The resulting application could be developed further to an extent where it would require minimum human intervention to extract crucial information from a resume, such as an applicant’s work experience, name, geographical location.
We would also attempt to build a simple version of a resume parser. We would be using nltk for NLP (natural language processing) tasks such as stop word filtering and tokenization, and pdfminer for extracting text from PDF formats.