An Empirical Study of Representation, Training and Decoding for Span-based Named Entity Recognition

Anonymous

Paper Link: https://openreview.net/forum?id=uympaMgttf

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract: Named Entity Recognition (NER) is an important task in Natural Language Processing with application 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 maximize a global score, given a list of candidate spans. We perform our study on three benchmarks NER datasets from different domains. The code and supporting files for the experiments will be made publicly available.