Efficient navigation in learning materials: An empirical study on the linking process

Abstract

We focus on the task of linking topically related segments in a collection of documents. In this scope, an existing corpus of learning materials was annotated with links between its segments. Using this corpus, we evaluate clustering, topic models, and graph-community detection algorithms in an unsupervised approach to the linking task. We propose several schemes to weight the word co-occurrence graph in order to discovery word communities, as well as a method for assigning segments to the discovered communities. Our experimental results indicate that the graph-community approach might BE more suitable for this task.