An unsupervised approach to automated selection of good essays

Evaluating essays automatically has been an area of active research for some time. In this paper, we propose an unsupervised technique to select a set of good essays from a large selection of essays written on the same topic. We use a `bag of words' approach which does not require deep parsing. The approach is based on the content of individual essays and the divergence of the individual essay from the collection when the collection is considered as one large essay. The approach is unsupervised and does not require any reference text to build computational learning model. We evaluate our approach on a set of essays, written by different people, on a single topic submitted to a competition internally within our organization. The approach enables selection of good essays which have a good correlation with the human based selection.