LIARc: Labeling Implicit ARguments in Spanish deverbal nominalizations

Abstract

This paper deals with the automatic identification and annotation of the implicit arguments of deverbal nominalizations in Spanish. We present the first version of the LIAR system focusing on its classifier component. We have built a supervised Machine Learning feature based model that uses a subset of AnCora-Es as a training corpus. We have built four different models and the overall F-Measure is 89.9%, which means an increase F-Measure performance approximately 35 points over the baseline (55%). However, a detailed analysis of the feature performance is still needed. Future work will focus on using LIAR to automatically annotate the implicit arguments in the whole AnCora-Es. © 2013 Springer-Verlag.