Warana: Recruitment Helper for Enterprises 
University of Moratuwa
At present, Natural Language Processing techniques are widely used in almost all the computer applications, in which language related processing procedures are being followed. This area of Language processing related work is still in the process of development. Although researchers and application developers try to embed Natural Language Processing modules increasingly with systems.
Warana Recruitment Helper is one such application which uses Natural Language Processing related technological aspects in order to avoid the difficulties, which are faced by the Recruiters at Enterprises during the employee recruitment process. According to the recent researches and surveys carried out by many parties, they have found that the process of recruitment is trying to minimize the time as possible. Researches show that the recruiters loos at the resumes of the candidates a very small amount of time. Statistically this value takes about 6-10 seconds.
We all know nowadays people maintain their online profiles such as LinedIn, Blogosts, etc. At the recruitment process, recruiters observe these profiles in order to get a wider picture about the candidate. Although this is the trend, this process slows down the recruitment process. When the time required increases, attention paid to capture valuable information further decrease. If this kind of process can be automated and provide analytical details about the candidate in an impressive manner, then the process of recruitment not only becomes easier, but also allow the recruiter to have wider set of details about the candidates.
Warana Recruitment Helper is an application, which pays attention towards afore mentioned facts as well as the best solution for the problem. Warana Recruitment Helper's focus is to map the candidates with the company requirements and generate a comparable score to evaluate the candidates. As a proof of concept of this fact, in this initial solution we are focusing on the domain of IT Industry and evaluate the candidates based on their technical proficiencies compared to the technological requirements of the company.
Keywords: Natural Language Processing | Machine Learning / Deep Learning |