BiLSTM for Resume Classification

In the era of digital recruitment and increasing volumes of job applications, the effective categorization and classification of resumes have become essential for streamlining the hiring process. The purpose of this paper is to present a Bidirectional LSTM architecture method designed to enhance the accuracy and efficiency of resume screening and classification. Leveraging the power of the presented bidirectional LSTM architecture allows the network to effectively capture complex information and context. To enhance the model's performance, we also incorporate word embedding, further enriching textual data representation. We evaluate the proposed method using a comprehensive dataset of resumes across various industries and job roles, demonstrating its superior performance in terms of classification accuracy and speed compared to traditional methods. Furthermore, we discuss the potential applications of this method in recruitment automation and offer insights into its scalability and adaptability in real-world scenarios, providing a valuable tool for human resource (HR) professionals and recruitment agencies seeking to optimize their hiring processes.

I. Introduction

In the ever-evolving landscape of human resources and job markets, efficient and accurate resume classification has become a critical task for organizations seeking to identify the most suitable candidates for a wide array of positions [1]. In this paper, we present a novel resume classification method founded on a Bidirectional LSTM classification techniques, designed to enhance the precision and efficacy of candidate evaluation in the recruitment process. The volume of resumes received by organizations has grown exponentially, making manual sorting and evaluation a daunting and time-consuming challenge. Consequently, there is a pressing need for automated solutions that can quickly and accurately filter resumes, thereby enabling hiring professionals to focus their efforts on the most qualified candidates [2]-[4]. During this study, we have demonstrated how we can utilize a Bidirectional LSTM architecture to create a strong, accurate classifier that can distinguish resume attributes and map them to specific job requirements by integrating boosting into the resume classification process. A description of the methodology behind a Bidirectional LSTM classification framework will be presented in the following sections, delving into the technical details of the framework as it applies to resume classification.