Natural Language Processing Applications in HR Software

A company’s HR department holds a unique role that is entirely centered around the employees’ experience. Not only do these functions span over the length of the employee’s tenure, it even covers their involvement from the moment candidates are considered for the job. 

As companies expand in terms of employed staff and operations, it becomes increasingly difficult to maintain a consistently efficient approach to HR functions. It’s when tasks evolve beyond the capacity of the HR department that companies should look to the various tech solutions and work to optimize as much of these functions as possible. 

Natural Language Processing is one of the most promising technologies for HR departments in the coming year and it has already cultivated global interest through sheer potential.

Natural Language processing technology refers to a computer or software’s ability to comprehend language, be it spoken or written. It’s the foundation of human-computer interaction and is widely used in various ways, chief among them the normalized AI products that we use every day.

For example, it’s NLP that allows Siri to understand what you mean when you ask it to tell you a joke or locate some information. It’s NLP that goes into your Alexa interactions, your auto-complete search function, and even spell-checking applications like Grammarly.

IT doesn’t seem quite so complex when considered in an auto-correct, auto-complete, or spell-checking capacity, but NLP as a technology shows immense potential for future implementations both in HR and beyond. In fact, the majority of insights regarding HR tech trends for 2020 name NLP as an innovative factor with imminent growth and undeniable effect.  However, for the time being, we’ve only scratched the surface of what it can do. And it allows us to optimize HR functions in different ways. 

NLP can be utilized to gain better control over the flood of data that HR departments deal with in regards to screening applicants, analyzing resumes, and onboarding new employees.

Seeing as perfecting recruitment journeys is at the very core of what we do at Manatal, adopting NLP was a very important pillar for our software as it is today. A lot of our ATS functions utilize this technology to save time and ease the effort of an HR department’s involvement.

In recruitment, the candidate information received on a daily basis, be it in a recruiting agency setting or HR department represents a challenge of analysis, data extraction, and evaluation. This process often consumes your HR’s time on a large scale, so much so that it makes time management increasingly difficult. 

To put this into perspective, we’ve outlined Manatal’s NLP functions below

The ability to create and customize candidate profiles is one of the basic Manatal features that allow our users to optimize their recruitment. When candidates are added to the platform, their profile is built using data sourced directly from their resume. 

NLP technology analyzes the resume and is able to extract details that pertain directly to the applicant’s value as a candidate for a specific job. It’s able to understand and extract data such as previous employment title and employer details, specific skills, and spoken languages, and is even capable of understanding the nationality and origins of the candidate in question.

Wherever there is AI, there is NLP. For an AI to truly comprehend data and requests at the level that defines it, it requires the usage of NLP on a fundamental level. This is also true for Manatal’s AI recommendation system, one of the many features that set our recruiting software apart. There are two sides to what this technology does for Manatal in this regard.

The first pertains to the job, the second to the candidate.

When users add a job to their Manatal account and specify its details in the description, the AI system is able to read and comprehend exactly what kind of candidates the user is looking to recruit. The NLP analysis extracts all criteria and requirements to set the bar that candidates need to meet in order to qualify for the position. 

Once the criteria are set, the AI system analyzes candidates in the database and filters out qualified talent from the rest. The profiles that meet its set criteria are then analyzed for professional advantage, special skills, and relevant past experience. These candidates are scored and rated for competence and fit, offering users a concise and short list of finalists for absolute clarity.

For the time being, NLP’s role is to aid users by automating the analysis and recommendations of capable candidates. But its potential for later stages such as interviews, onboarding, and training is among the projections that the recruitment world anticipates to see in the coming months.