Applying latent Dirichlet allocation to automatic essay grading

Abstract

We report experiments on automatic essay grading using Latent Dirichlet Allocation (LDA). LDA is a "bag-of-words" type of language modeling and dimension reduction method, reported to out-perform other related methods, Latent Semantic Analysis (LSA) and Probabilistic Latent Semantic Analysis (PLSA) in Information Retrieval (IR) domain. We introduce LDA in detail and compare its strengths and weaknesses to LSA and PLSA. We also compare empirically the performance of LDA to LSA and PLSA. The experiments were run with three essay sets consisting in total of 283 essays from different domains. On contrary to the findings in IR, LDA achieved slightly worse results compared to LSA and PLSA in the experiments. We state the reasons for LSA and PLSA outperforming LDA and indicate further research directions. © Springer-Verlag Berlin Heidelberg 2006.