Autores
Angel Gil Jason Efrain
Aroyehun Segun Taofeek
Gelbukh Alexander
Título NLP-CIC @ PRELEARN: Mastering prerequisites relations, from handcrafted features to embeddings
Tipo Congreso
Sub-tipo Memoria
Descripción 7th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop, EVALITA 2020
Resumen We present our systems and findings for the prerequisite relation learning task (PRELEARN) at EVALITA 2020. The task aims to classify whether a pair of concepts hold a prerequisite relation or not. We model the problem using handcrafted features and embedding representations for in-domain and cross-domain scenarios. Our submissions ranked first place in both scenarios with average F1 score of 0.887 and 0.690 respectively across domains on the test sets. We made our code freely available. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Observaciones
Lugar Virtual, online
País Indefinido
No. de páginas
Vol. / Cap.
Inicio 2020-12-17
Fin
ISBN/ISSN