Abstract
Named entity recognition (NER) is one of the
best studied tasks in natural language processing. However, most approaches are not capable of handling nested structures which are
common in many applications. In this paper
we introduce a novel neural network architecture that first merges tokens and/or entities into
entities forming nested structures, and then labels each of them independently. Unlike previous work, our merge and label approach
predicts real-valued instead of discrete segmentation structures, which allow it to combine word and nested entity embeddings while
maintaining differentiability. We evaluate our
approach using the ACE 2005 Corpus, where
it achieves state-of-the-art F1 of 74.6, further
improved with contextual embeddings (BERT)
to 82.4, an overall improvement of close to
8 F1 points over previous approaches trained
on the same data. Additionally we compare
it against BiLSTM-CRFs, the dominant approach for flat NER structures, demonstrating
that its ability to predict nested structures does
not impact performance in simpler cases.