# Job Prediction: From Deep Neural Network Models to Applications

arXiv: https://arxiv.org/abs/1912.12214 · HTML: https://mlsanity.com/paper/1912.12214/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Predicting Job Titles from Job Descriptions with Multi-label Text Classification](https://mlsanity.com/paper/2112.11052/) (2021)
- [Transformer-Based Contextualized Language Models Joint with Neural Networks for Natural Language Inference in Vietnamese](https://mlsanity.com/paper/2411.13407/) (2024)
- [ViTHSD: Exploiting Hatred by Targets for Hate Speech Detection on Vietnamese Social Media Texts](https://mlsanity.com/paper/2404.19252/) (2024)

## Abstract (arXiv)

Determining the job is suitable for a student or a person looking for work based on their job's descriptions such as knowledge and skills that are difficult, as well as how employers must find ways to choose the candidates that match the job they require. In this paper, we focus on studying the job prediction using different deep neural network models including TextCNN, Bi-GRU-LSTM-CNN, and Bi-GRU-CNN with various pre-trained word embeddings on the IT Job dataset. In addition, we also proposed a simple and effective ensemble model combining different deep neural network models. The experimental results illustrated that our proposed ensemble model achieved the highest result with an F1 score of 72.71%. Moreover, we analyze these experimental results to have insights about this problem to find better solutions in the future.