Essay

Object: Automated essay scoring is the computer tech-niques and algorithms that evaluate and score essays automat-ically. Compared with human rater, automated essay scoring has the advantage of fairness, less human resource cost and timely feedback. In previous work, automated essay scoring is regarded as a classification or regression problem. Machine learning techniques such as K-nearest-neighbor (KNN), multi-ple linear regression have been applied to solve this problem. In this paper, we regard this problem as a ranking problem and apply a new machine learning method, learning to rank, to solve this problem. We will introduce detailed steps about how to apply learning to rank to automated essay scoring, such as feature extraction, scoring. Experiments in this paper show that learning to rank outperforms other classical machine learning techniques in automated essay scoring.

Keywords-Automated essay scoring; Learning to rank; Fea-ture extraction; Machine learning;

I. INTRODUCTION

Automated Essay Scoring (AES) is defined as the com-puter techniques and algorithms that evaluate and score essays automatically [3]. In this paper, we mainly discuss automated essay scoring on English essays. In recent years, with the growing need of essay scoring in large-scale English test and in the teaching of English writing skill, automated essay scoring has become a hot issue in the research of natural language processing. Nowadays, large-scale English test has been widely spread in the world such as GRE, GMAT, TOEFL [1]. On one hand, the essay scoring task in such test costs huge human resources but the efficiency is low. On another hand, the essay score given by human rater is mostly determined by rater’s personal will, emotion and energy. An essay scored highly by one rater may recieve a low score from another rater. Even the same rater probably gives different scores for the same essay at different times. Thus, the fairness of essay scoring cannot