Global Span Selection for Named Entity Recognition
Urchade Zaratiana, Niama Elkhbir, Pierre Holat, Nadi Tomeh, Thierry Charnois · 2022
Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains.In this paper, we describe a novel approach to named entity recognition, in which we output a set of spans (i.e., segmentations) by maximizing a global score.During training, we optimize our model by maximizing the probability of the gold segmentation.During inference, we use dynamic programming to select the best segmentation under a linear time complexity.We prove that our approach outperforms CRF and semi-CRF models for Named Entity Recognition.We make our code publicly available at https://github.com/urchade/ global-span-selection.