Abstract
Now-a-days, the use of web portals known as job boards for publishing job offers by recruiters has grown consid-erably. The candidates in their turn, apply to the job positions via the job boards. Since the opportunities are available on a wide range and the job application process is fast and straightforward, the data flow is transformed to large-volume data sets which are hard to handle. Most companies tend to automate the candidate selection process that aims to match the job offers with suitable resumes. In this paper, we propose a supervised learning approach to classify the job offers and CVs shared in the recruitment sites in order to enhance automatic recruitment process. We used natural language processing techniques for job offers and CV preprocessing. Next, we used word embeddings and deep neural networks to train two models, the first one categorizes recruitment documents based on job skills, and the second one predicts the expertise degree class. The experiment results show that our proposal is very efficient.
Highlights
Recruitment is the process of searching and selecting personnel for job positions in the staff of a company [1]
We propose a solution for text classification using deep neural networks and word embeddings for textual documents in the IT recruitment domain
While [15] proposed a technique to enhance the document representation basing on the Naive Bayes method, which estimates the conditional probabilities of Naive Bayes using a deep feature weighting method for text classification
Summary
Recruitment is the process of searching and selecting personnel for job positions in the staff of a company [1]. The job offer and candidate resume are unstructured textual documents that could have multiple formats These documents need a delicate process before applying the machine learning algorithms for achieving the classification task [4],[5]. The first phase in this process is the preprocessing task that aims to reduce the multiple forms of words in order to extract relevant features to feed the classification algorithms It reduces the dimensionality of the resulted features, which is an essential part of building an efficient model [6]. We aim to classify these documents according to two levels, the first one concerns professional skills and the second is about the degree of expertise These levels are combined to divide the huge IT jobs databases into smaller subsets to make their processing more easier.
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