Two-Stage Framework for Job Title Identification System in Online Recruitment

DOI

10.2991/978-94-6463-858-5_269How to use a DOI?

Keywords

Natural Language Processing (NLP); Text Preprocessing; Classification; Logistic regression; BERT; Support Vector Machines (SVM)

Abstract

The “Two-Stage framework for Job Title Identification System in Online recruitment” presents an approach to accurately extracting and classifying job titles from online job advertisements using a two-stage process. This system addresses the challenge of parsing and understanding job titles from diverse and unstructured job postings available on the internet. In the first stage, the system uses natural language processing (NLP) techniques to preprocess and clean the text data and normalize them to ensure that the textual data are in a suitable format for further job title candidates from the job description and other relevant sections of the advertisement. The second stage involves a classification model that accurately identifies and categorizes job titles from the candidate set generated in the first stage. The model uses machine learning algorithms, such as support vector machines (SVM), random forests, or deep learning approaches, to distinguish between valid job titles and non-title text. The classification process is trained on a comprehensive dataset of labeled job titles and advertisements, enabling the system to learn and generalize from various job title patterns and formats. The experimental results demonstrate that our two-stage methodology improves job title identification accuracy by achieving 94.55% in certain sectors.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - N. Nikhitha
AU  - M. Prasad Naidu
AU  - Shaik Asif Ali
AU  - M. Raman Kumar
PY  - 2025
DA  - 2025/11/04
TI  - Two-Stage Framework for Job Title Identification System in Online Recruitment
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3232
EP  - 3243
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_269
DO  - 10.2991/978-94-6463-858-5_269
ID  - Nikhitha2025
ER  -