Automatic Annotation of Co-Occurrence Relations

We introduce a method for automatically labelling edges of word co-occurrence graphs with semantic relations. Therefore we only make use of training data already contained within the graph. Starting point of this work is a graph based on word co-occurrence of the German language, which is created by applying iterated co-occurrence analysis. The edges of the graph have been partially annotated by hand with semantic relationships. In our approach we make use of the commonly appearing network motif of three words forming a triangular pattern. We assume that the fully annotated occurrences of these structures contain information useful for our purpose. Based on these patterns rules for reasoning are learned. The obtained rules are then combined using Dempster-Shafer theory to infer new semantic relations between words. Iteration of the annotation process is possible to increase the number of obtained relations. By applying the described process the graph can be enriched with semantic information at a high precision.