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Hybrid Approach to Automated Essay Scoring: Integrating Deep Learning Embeddings with Handcrafted Linguistic Features for Improved Accuracy

Automated Essay Scoring (AES) systems face persistent challenges in delivering accuracy and efficiency in evaluations. This study introduces an approach that combines embeddings generated using RoBERTa with handcrafted linguistic features, leveraging Lightweight XGBoost (LwXGBoost) for enhanced scor...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Muhammad Faseeh, Abdul Jaleel, Naeem Iqbal, Anwar Ghani, Akmalbek Abdusalomov, Asif Mehmood, Young-Im Cho
Format: Artigo
Sprache:Inglês
Veröffentlicht: MDPI AG 2024-10-01
Schriftenreihe:Mathematics
Schlagworte:
automated essay scoring
RoBERTa embeddings
handcrafted features
LwXGBoost
RoBERTa
Online-Zugang:https://www.mdpi.com/2227-7390/12/21/3416
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