Finding candidates’ closet skeletons with help of artificial intellect

Today, we can witness a rapid growth of headhunting practices, as any company, a mid-size or a large-size one, wants to attract to their team savvy experts who will bring considerable benefits. However, sometimes it’s not so easy to find out the suitable candidates, because a seemingly experienced and leading specialist may hide some valuable info, have so-called skeletons that will be certainly of interest for your company’s HR managers who are trying to close one or another job offer.    

How to get trustworthy and detailed info on the candidate just having his/her self-typed CV? Here one can make use of modern technologies that will help “to solve a mystery”.

The right hands for searching and analyzing additional data about potential employees may be artificial intellect, i.e. specific tools based on natural language linguistic APIs that become a real impediment for hiding something in the global net. 

Which linguistic tools are meant here?

1) Named Entity Recognizer – the title is self-explanatory: from thousands of documents, social feeds and other info stored in the net, this tool can “define and extract” your candidate by name and surname and reveal fresh and useful information, for example, that connected with previous employers’ reviews on the candidate and his/her activities.

Furthermore, additional info can be found not only by potential employee’s name, but also by the company’s name he/she used to work in.

Such a search is possible thanks to the combination of different algorithms, including statistical model based on hidden Markov Model, machine learning algorithms (that automatically originate named entity recognition patterns), and manually created expert rules that allow improving statistical data.

To function correctly, this tool needs a suitable API (application programming interface). This survey reveals the comparison of a wide range of APIs, including the Google Cloud Natural Language API that is supposed “to kill the market” and some others, more affordable APIs, like Intellexer or Aylien. 

2) Categorizer is also built on a machine learning technique. It deals with analyzing textual information and creating an array of categories, connected with the potential candidate. This tool allows to obtain more detailed info on the applicant and find the relation between categories parts. 

Moreover, such Categorizer can be applied to analyze CMS, corporate CRM information about your present employees, to conduct corporate data analysis, etc.

3) Sentiment Analyzer is another sophisticated tool; it deals with sentiments extraction and allows to grasp emotions and evaluative statements about a certain object (in our case about the candidate) and to examine thoroughly one more time the relevant info, including reference letters and reviews.

Beyond that, the analysis of such biased statements can be applied in relation to the applicant’s previous work/projects.  

4) Question-Answering System is an innovative tool that grants a possibility to make questions and get answers on natural language. Thus, you’ll be able to get the necessary responses about your future employees and his/her activities. This linguistic tool can be also implemented to carry out research activities in the Internet, databases (including e-libraries and catalogues). To cite another example, thanks to this instrument your present employees can get relevant info on the company policy.

5) Comparator gives a chance to compare two different documents, one document with a multitude, or a multitude with a multitude. As for its implementation in the HR sphere and the analysis of all the relevant info on applicants you’re interested in, the tool can compare CVs placed by the candidate on various websites to define the working experience relevancy. Furthermore, your HR managers can compare an enormous number of different CVs to determine plagiarism/clichés or some unique features in CVs.     

Conclusion

It seems that linguistic tools can be applied in an array of spheres, including HR, and assist in choosing the right candidate to strengthen the company’s team. The key issue here is to integrate these tools thanks to the help of experts in business applications development to conduct information analysis in a suitable way.  

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