par Gridach, Mourad;Haddad, Hatem 
Référence Lecture notes in computer science, 10761 LNCS, page (264-275)
Publication Publié, 2018
Référence Lecture notes in computer science, 10761 LNCS, page (264-275)
Publication Publié, 2018
Article révisé par les pairs
| Résumé : | The previous Named Entity Recognition (NER) models for Modern Standard Arabic (MSA) rely heavily on the use of features and gazetteers, which is time consuming. In this paper, we introduce a novel neural network architecture based on bidirectional Gated Recurrent Unit (GRU) combined with Conditional Random Fields (CRF). Our neural network uses minimal features: pretrained word representations learned from unannotated corpora and also character-level embeddings of words. This novel architecture allowed us to eliminate the need for most of handcrafted engineering features. We evaluate our system on a publicly available dataset where we were able to achieve comparable results to previous best-performing systems. |
