Named Entity Recognition as Structured Span Prediction
Urchade Zaratiana, Nadi Tomeh, Pierre Holat, Thierry Charnois · 2022
Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains.While the dominant paradigm of NER is sequence labelling, span-based approaches have become very popular in recent times but are less well understood.In this work, we study different aspects of span-based NER, namely the span representation, learning strategy, and decoding algorithms to avoid span overlap.We also propose an exact algorithm that efficiently finds the set of non-overlapping spans that maximizes a global score, given a list of candidate spans.We performed our study on three benchmark NER datasets from different domains.We make our code publicly available at https://github.com/urchade/ span-structured-prediction.