Aina Peris

Annotating the argument structure of deverbal nominalizations in Spanish

Over recent years, there has been a growing interest in the computational treatment of nominalized Noun Phrases due to the rich semantic information they contain. These Noun Phrases can be understood as verbal paraphrases and, just like them, they can also denote argument and thematic-role relations. This paper presents the methodology followed to annotate the argument structure of deverbal nominalizations in the Spanish AnCora-Es corpus. We focus on the automated annotation process that is mostly based on the semantic information specified in a verbal lexicon but also on the syntactic and semantic information annotated in the corpus. The heuristic rules that make use of this information rely on linguistic assumptions that are also evaluated as we evaluate the reliability of the automated process. The automated annotation was manually checked in order to ensure the accuracy of the final resource. We demonstrate its feasibility (77% F-measure) and show that it facilitates corpus annotation, which is always a time-consuming and costly process. The result is the enrichment of the AnCora-Es corpus with the argument structure and thematic roles of deverbal nominalizations. It is the first Spanish corpus with this kind of information that is freely available.

LIAR c : labeling implicit ARguments in spanish deverbal nominalizations

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.

Empirical methods for the study of denotation in nominalizations in spanish

This article deals with deverbal nominalizations in Spanish; concretely, we focus on the denotative distinction between event and result nominalizations. The goals of this work is twofold: first, to detect the most relevant features for this denotative distinction; and, second, to build an automatic classification system of deverbal nominalizations according to their denotation. We have based our study on theoretical hypotheses dealing with this semantic distinction and we have analyzed them empirically by means of Machine Learning techniques which are the basis of the ADN-Classifier. This is the first tool that aims to automatically classify deverbal nominalizations in event, result, or underspecified denotation types in Spanish. The ADN-Classifier has helped us to quantitatively evaluate the validity of our claims regarding deverbal nominalizations. We set up a series of experiments in order to test the ADN-Classifier with different models and in different realistic scenarios depending on the knowledge resources and natural language processors available. The ADN-Classifier achieved good results (87.20% accuracy).