4 Key Resume Parsing Techniques and Why You Only Need the Last One

There are many resume parsing techniques out there, but they don't all perform to the same standard. Here's the best option for your recruiting software.
5 minutes
When you've got the right resume parsing technique, you don't need any others
When you've got the right resume parsing technique, you don't need any others

Most people these days know what a resume parser is, but did you know that there is a lot of variation in the techniques used in resume parsing? Each one has unique features and different levels of speed and accuracy. Your organisation is one of a kind and has unique requirements; let’s explore which might be the right resume parsing technique for your needs.

4 Key Resume Parsing Techniques

  1. Keyword-Based Parsers

Think of keyword-based resume parsers as a prototype of the faster and more accurate parsers we see more often today. These simplistic parsers look for specific words within the text of a resume, as well as key phrases and patterns.

For example, if the parser is looking for a year of employment, it may be looking for a pattern with four numbers in a row. However, this is prone to error as that number could also be a birth year, postcode, or something else!

The same goes for words that have more than one application in a CV, such as ‘marketing’ – this could be part of a job title, a skill, work experience, or come under any number of other categories! Keyword parsers are just looking for the keyword and struggle to interpret the information around that keyword to discover the semantics behind how it is used. Therefore, information is frequently mislabelled or interpreted incorrectly.

You can expect an accuracy rate of around 70% from keyword-based parsing software. 

  1. Grammar-Based Parsers

Grammar-based parsers rely on the use of a number of grammatical rules to interpret information. These tools scour resume data to find the various contexts of the same word and understand the meaning of each. They combine phrases and words to uncover the meaning of each sentence. 

These relatively complicated parsers require a lot of manual input during the encoding process. If coded by a language engineer with the right skills, they can analyse a resume pretty well. However, if the manual set-up is not done well, grammar-based parsers can be highly inaccurate.

You can expect an accuracy rate of around 90% from a high-quality grammar-based resume parser.

  1. Statistical-Based Parsers

What do statistics have to do with understanding the context of phrases in a resume? Statistical parsers work by applying numerical models of text to identify the key elements of a CV. Essentially, they extract the desired information by figuring out which context a word or phrase is being used in. For example, these tools may be able to tell if the word ‘coordination’ is a skill or job task. 

For statistical parsers to be accurate, they need to have processed a large number of resumes containing all of the information that requires extraction. This is a training process that occurs before any real CVs can be submitted to it.

Statistical parsers, in terms of accuracy, fall somewhere in the middle of the low accuracy levels of keyword parsers and the higher accuracy of grammatical parsers. 

  1. AI-Based Parsers

While AI technology may seem new to some people, it has actually been around for quite a while now. As artificial intelligence has evolved, so have the abilities of many AI-based parsers. They can now leverage machine learning, meaning that these models can improve over time as they parse more information. 

This type of resume parsing technology attains an extremely high level of accuracy when compared with the other resume parsing techniques in the market. 

Why You Only Need AI Parsing

Human-Level Understanding

Affinda’s resume parser is not keyword-based, grammar-based, or statistical. We use AI with language understanding superior to a human’s. 

The latest AI developments mean that our AI can now understand language even better than humans can. Take a second to think about that – you explain something to a friend, and they don’t quite get what you’re saying. Explain the same thing to an AI, and it will understand you perfectly!

SuperGLUE is a benchmark created specifically for checking the language understanding of AI technologies. Any new AI models are now tested against the SuperGLUE benchmark to see how good they are at completing language tasks.

The significant breakthrough came just recently in 2021 when results finally showed that machines are now understanding language better than humans!


Understanding Information Layers

One piece of text in a resume can mean many different things. When reading resumes, people can usually distinguish what a word or phrase means based on the context of what’s around it. However, when traditional parsers analyse a resume extract, they find it almost impossible to understand these semantics. 

Instead, recent AI technologies understand that information can be viewed in more than one way. For example, a word like ‘communication’ could be a skill, experience, or contained in a job title. This is known as ‘Information Layers’; i.e. one piece of information can be included in many Information Layers.

To understand the full meaning of a resume, each layer of information must be understood – and AI parsers are by far the best at this aspect of information extraction and analysing data.

Picks Up On Spatial Cues

One of the ways that humans view the world is in the context of how close or far apart things are from each other. For example, a large phrase with a smaller phrase placed right underneath is likely to be a heading and a subtitle. AI technology can now pick up on these types of spatial cues as well when reading a CV. 

Another example is that multiple roles are likely to be placed in a resume one after the other, without different information in between. AI technology being able to recognise cues like these has been revolutionary for the accuracy of its resume parsing work.

Why You Need Affinda’s Resume Parser

One common challenge of resume parsing is the cost investment required, especially if you want to access software that leverages ground-breaking AI resume parsing techniques, like most of our customers do. This can be a roadblock for many businesses that would like to improve their recruitment workflows.

That’s why, here at Affinda, we are focused on keeping our products at a competitive price point. Many businesses who enquire about our prices are pleasantly surprised to find them quite affordable, especially when comparing the quality of our resume parser to other options on the market. 

One happy customer had this to say:

“We evaluated four competing SAAS solutions, and after the evaluation we found that [Resume Parser by Affinda] scored best on quality, service and price…”

If you’re keen to work with a software company full of friendly people who don’t talk over your head with tech-speak, Affinda is the one for you! We are known for our fast response times and helpful advice. We also love getting feedback from our customers, frequently using your comments and concerns to develop new product features. 

Why not join the hundreds of happy customers who are already using Affinda’s parsing tool to transform their recruitment processes? If you’re interested in knowing more about how to use our product, give us a call today. Someone on our team will be happy to answer any questions you may have and explain more about how the AI resume parsing technique works.  

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