Automatic Thai-Language Essay Scoring Using Neural Network and Latent Semantic Analysis

In this research, a backpropagation neural network and latent semantic analysis were used to assess the quality of Thai-language essays written by high school students in the subject matter of historical royal Thai literatures. Forty essays written in response to a question were each evaluated by high school teachers and assigned a human score. In the first experiment, we used raw term frequency vectors of the essays and their corresponding human scores to train the neural network and obtain the machine scores. In the second experiment, we pre-processed the raw term frequency vectors using latent semantic analysis technique prior to feeding them to the neural network. The experimental results show that the addition of latent semantic analysis technique improves scoring performance