2026-04-17T07:01:30Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/103742025-07-17T14:27:40Zcom_2072_1033col_2072_452950

urn:hdl:2117/10374 ADN-classifier: automatically assigning denotation types to nominalizations Peris, Aina Taulé, Mariona Boleda Torrent, Gemma Rodríguez Hontoria, Horacio Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic Spanish Deverbal Nominalizations (Classification system) ADN-Classifier (Automatic classification system) Natural language processing (Computer science) Computational linguistics -- Research Lingüística computacional Corpus (Lingüística) Castellà -- Lexicografia 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. Peer Reviewed Postprint (published version) 2010 Conference report Open Access