Why You Need a Resume Parser Using Natural Language Processing

Natural language processing (NLP) is the driving force behind Affinda's new resume parser, a surprisingly precise tool which can make sense out of bulk unorganized data.

NLP AI  is the secret behind our resume parsing success
Natural Language Processing (NLP) AI is the secret behind more effective resume parsing

It has long been a challenge for software developers to create resume parsing tools that are accurate, efficient, and can detect all the information that recruiters need. That said, we are proud to introduce Affinda’s resume parser using natural language processing (NLP).  We have finally succeeded in creating an incredibly precise resume parser that makes it easy to extract data from countless pages of unstructured information.

What is Natural Language Processing (NLP) AI?

Natural language processing software screens information to determine the semantics behind the words on a page. In other words, it uses artificial intelligence to detect the true meaning behind what people have written.

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.

Our natural language processing AI analyses what is written to determine what the writer meant and sort the data into the appropriate category. This means that we can gain accurate data even from information that has a high level of variability.

Using NLP to parse highly different resumes
The NLP AI Affinda is built on allows it to accurately parse data that has a high level of variability

What Makes Our Resume Parser Different?

Resume Specific Software

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.

Combination of Techniques

Instead of using just one method to gather data, Affinda uses a combination of approaches and technologies. In addition to using natural language processing, we also use image-based object detection methods to discover and sort information. This data then goes through named entity recognition to output the data for key fields.

Benefits of Our Resume Parser Using NLP AI

More Accurate Parsing

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.

As a result, Affinda is highly accurate because it can detect meaning, not just words.

Sort Text into Predefined Categories

Applicants often write about their experience and skills in sentences instead of lists of keywords. Field-based parsing software cannot pick up on these sentences and extract the information from them. By connecting Affinda to your ATS, you can populate the fields you need to fill in far more easily.

Turn Information into Data

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.

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