Utilizing Latent Semantic Word Representations for Automated Essay Scoring

• Published in: 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom) pp. 1101 - 1108
• Main Authors: Cancan Jin, Ben He
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-08-2015
• Subjects: Automated essay scoring; Complexity theory; Deep neural network; Feature extraction; Grammar; Natural language processing; Semantic word representations; Semantics; Syntactics
• DOI: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.202

Automated essay scoring (AES) utilizes a set of features to measure the writing quality of essays. However, due to the limits of the existing natural language processing techniques, current AES systems are only capable of making use of shallow text features such as the essay length and the number of the clause. In this paper, we argue that the current AES systems can be further improved by taking into account the latent semantic features. To this end, on top of the commonly used shallow features, we propose three deep semanitc features based on Continuous Bag-of-Words Model (CBOW) and Recursive Auto encoder Model. We use Support Vector Machine for Ranking (SVM rank ) to learn a rating model and test the performance of three new features. Experiments on the publicly available English essay dataset, Automated Student Assessment Prize (ASAP), show that our proposed features are beneficial to automated essay scoring.