Parameter optimisation for memory-based cross-lingual word-sense disambiguation

Abstract

We present our system WSD2 which participated in the Cross-Lingual Word-Sense Disambiguation task for SemEval 2013 (Lefever and Hoste, 2013). The system closely resembles our winning system for the same task in SemEval 2010. It is based on k-nearest neighbour classifiers which map words with local and global context features onto their translation, i.e. their cross-lingual sense. The system participated in the task for all five languages and obtained winning scores for four of them when asked to predict the best translation(s). We tested various configurations of our system, focusing on various levels of hyperparameter optimisation and feature selection. Our final results indicate that hyperparameter optimisation did not lead to the best results, indicating overfitting by our optimisation method in this aspect. Feature selection does have a modest positive impact.