The second Maria de Maeztu Strategic Research Program (CEX2021-001195-M) of the Department of Information and Communication Technologies (DTIC) takes place between 2023 and 2026. The website for this program is under construction. You can find some details in this news.

The first María de Maeztu Strategic Research Program (MDM-2015-0502) took place between January 2016 and June 2020. It was focused on data-driven knowledge extraction, boosting synergistic research initiatives across our different research areas.

Back Ferres D, AbuRa'ed A, Saggion H. Spanish Morphological Generation with Wide-Coverage Lexicons and Decision Trees. Procesamiento del Lenguaje Natural

FERRÉS, Daniel; ABURA'ED, Ahmed; SAGGION, Horacio. Spanish Morphological Generation with Wide-Coverage Lexicons and Decision Trees. Procesamiento del Lenguaje Natural, [S.l.], v. 58, p. 109-116, mar. 2017. ISSN 1989-7553

 

Morphological Generation is the task of producing the appropiate inected form of a lemma in a given textual context and according to some morphological features. This paper describes and evaluates wide-coverage morphological lexicons and a Decision Tree algorithm that perform Morphological Generation in Spanish at state-of-the art level. The Freeling, Leffe and Apertium Spanish lexicons, the J48 Decision Tree algorithm and the combination of J48 with Freeling and Leffe lexicons have been evaluated with the following datasets for Spanish: i) CoNLL2009 Shared Task dataset, ii) Durrett and DeNero dataset of Spanish Verbs (DDN), and iii) SIGMORPHON 2016 Shared Task (task-1) dataset. The results show that: i) the Freeling and Leffe lexicons achieve high coverage and precision over the DDN and SIGMORPHON 2016 datasets, ii) the J48 algorithm achieves state-of-the-art results in all of the three datasets, and iii) the combination of Freeling, Leffe and the J48 algorithm outperformed the results of our other approaches in the three evaluation datasets, improved slightly the results of the CoNLL2009 and SIGMORPHON 2016 reported in the state-of-the-art literature, and achieved results comparable to the ones reported in the state-of-the-art literature on the DDN dataset evaluation.

Additional material.

 

 

Department of Information and Communication Technologies, UPF

Grant CEX2021-001195-M funded by MCIN/AEI /10.13039/501100011033


 


Department of Information and Communication Technologies, UPF

[email protected]

  • Àngel Lozano - Scientific director
  • Aurelio Ruiz - Program management