Using Pseudowords for Algorithm Comparison: An Evaluation Framework for Graph-based Word Sense Induction
In this paper we define two parallel data sets based on pseudowords, extracted from the same corpus. They both consist of word-centered graphs for each of 1225 different pseudowords, and use respectively first-order co-occurrences and secondorder semantic similarities. We propose an evaluation framework on these data sets for graph-based Word Sense Induction (WSI) focused on the case of coarsegrained homonymy: We compare different WSI clustering algorithms by measuring how well their outputs agree with the a priori known ground-truth decomposition of a pseudoword. We perform this evaluation for four different clustering algorithms: the Markov cluster algorithm, Chinese Whispers, MaxMax and a gangplankbased clustering algorithm. To further improve the comparison between these algorithms and the analysis of their behaviours, we also define a new specific evaluation measure. As far as we know, this is the first large-scale systematic pseudoword evaluation dedicated to the induction of coarsegrained homonymous word senses.