HLTDI: CL-WSD Using Markov Random Fields for SemEval-2013 Task 10
Alex Rudnick, Can Liu, Michael Gasser · 2013
We present our entries for the SemEval-2013 cross-language word-sense disambiguation task (Lefever and Hoste, 2013). We submitted three systems based on classifiers trained on local context features, with some elaborations. Our three systems, in increasing order of complexity, were: maximum entropy classifiers trained to predict the desired targetlanguage phrase using only monolingual features (we called this system L1); similar classifiers, but with the desired target-language phrase for the other four languages as features (L2); and lastly, networks of five classifiers, over which we do loopy belief propagation to solve the classification tasks jointly (MRF). 1