ADN-classifier: Automatically assigning denotation types to nominalizations
Aina Peris, Mariona Taulé, Gemma Boleda and Horacio Rodriguez
In: LREC 2010, 19-21 May 2010, Valletta, Malta.
This paper presents the ADN-Classifier, an Automatic classification system of Spanish Deverbal Nominalizations aimed at identifying
its semantic denotation (i.e. event, result, underspecified, or lexicalized). The classifier can be used for NLP tasks such as coreference
resolution or paraphrase detection. To our knowledge, the ADN-Classifier is the first effort in acquisition of denotations for
nominalizations using Machine Learning.We compare the results of the classifier when using a decreasing number of Knowledge
Sources, namely (1) the complete nominal lexicon (AnCora-Nom) that includes sense distictions, (2) the nominal lexicon
(AnCora-Nom) removing the sense-specific information, (3) nominalizations’ context information obtained from a treebank corpus
(AnCora-Es) and (4) the combination of the previous linguistic resources. In a realistic scenario, that is, without sense distinction, the
best results achieved are those taking into account the information declared in the lexicon (89.40% accuracy). This shows that the
lexicon contains crucial information (such as argument structure) that corpus-derived features cannot substitute for.