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FutureStarrResume Nlp OR
When you create a resume, you have nothing to lose. On the resume, you’re saying a story to the world. At the end of the day, a resume is your calling card for your portfolio of experience. Let go of the fear and pride, and give yourself a chance to make your story clear to employers.
As opposed to being a general data extraction parser, we have intentionally designed the Affinda resume parser to detect information from CVs. Our machine learning algorithms are not based only on NLP and data extraction; they have been trained using CV subsections like Education, Skills, and Work Experience. This allows us to gain a higher level of accuracy than many similar software options on the market.
Rather than simply seeking out keywords, as many resume parsers do, our product goes further to delve into the meaning behind the words. For instance, instead of seeking out a section titled ‘Skills’, the software uses machine learning to locate any point in the document where the writer mentions their skills and takes data from these sections. (Source: affinda.com)
Matching potential employees to employment opportunities is a challenging task, which has significant commercial value. Employment agencies, departments in companies concerned with human resources and small company owners frequently have to read, or process, numerous resumes before identifying a short list of candidates. Working with Talent Technology, a developer of recruitment and hiring software and component technology, the intern will develop solutions in several areas of automated resume processing. The proposed project will investigate how an integration of statistical machine learning and rule based techniques from the area of natural language processing can be used to automate the resume processing task, and result in better matching and ranking of candidates for particular job descriptions. The research aims to provide algorithms, methodology and software for the rapid processing of large volumes of resumes.
This project uses Python’s library, SpaCy to implement various NLP (natural language processing) techniques like tokenization, lemmatization, parts of speech tagging, etc., for building a resume parser in Python. And, considering all the resumes are submitted in PDF format, you will learn how to implement optical character recognition (OCR) for extracting textual data from the documents. The resulting application will require minimum human intervention to extract crucial information from a resume, such as an applicant’s work experience, name, geographical location, etc. It is one of the most exciting NLP projects for beginners, so make sure you attempt it. (Source: www.projectpro.io)
When job applicants apply for a position, they can describe exactly the same information in a number of ways. For example, the ‘Work History’ section of a CV could be called ‘Job History’, ‘Previous Employment’ or any of a number of other titles.
If you need to report on specific metrics for your applicants, a resume parser using NLP AI can provide you with data to analyse. It is near impossible to create such data-driven reports from blocks of information, but by turning your unstructured resume collection into data, reporting becomes easy and straightforward. Any field on a resume can be compared, analysed and visualizations created using basic data manipulation tools. For example, you’ll be able to see the percentage of men vs women applying for positions, the average length of roles, and more. Contact Affinda today to find out more about how NLP AI can help you to create a more data-driven recruitment process. (Source: affinda.com)