Authors :
Ankit Kumar
Volume/Issue :
Volume 6 - 2021, Issue 11 - November
Google Scholar :
http://bitly.ws/gu88
Scribd :
https://bit.ly/32Qc7tw
Abstract :
This paper analyses the Employment Scam
Aegean Dataset and compares various machine learning
algorithms including Logistic Regression, Decision Tree,
Random Forest, XGBoost, K-Nearest Neighbor, Naïve
Bayes and Support Vector Classifier on the task of fake job
classification. The paper also proposes two self-attention
enhanced Gated Recurrent Unit networks, one with vanilla
RNN architecture and other with Bidirectional
architecture, for classifying the fake job from real ones.
The proposed framework uses Gated Recurrent Units with
multi-head self-attention mechanism to enhance the long
term retention within the network. In comparison to the
other algorithms, the two GRU models proposed in this
paper are able to obtain better result.
Keywords :
Fake Job Classification; Text Classification; Gated Recurrent Unit; Recurrent Neural Networks.
This paper analyses the Employment Scam
Aegean Dataset and compares various machine learning
algorithms including Logistic Regression, Decision Tree,
Random Forest, XGBoost, K-Nearest Neighbor, Naïve
Bayes and Support Vector Classifier on the task of fake job
classification. The paper also proposes two self-attention
enhanced Gated Recurrent Unit networks, one with vanilla
RNN architecture and other with Bidirectional
architecture, for classifying the fake job from real ones.
The proposed framework uses Gated Recurrent Units with
multi-head self-attention mechanism to enhance the long
term retention within the network. In comparison to the
other algorithms, the two GRU models proposed in this
paper are able to obtain better result.
Keywords :
Fake Job Classification; Text Classification; Gated Recurrent Unit; Recurrent Neural Networks.
