Abstract
We propose two neural network architectures
for nested named entity recognition (NER), a
setting in which named entities may overlap
and also be labeled with more than one label.
We encode the nested labels using a linearized
scheme. In our first proposed approach, the
nested labels are modeled as multilabels corresponding to the Cartesian product of the
nested labels in a standard LSTM-CRF architecture. In the second one, the nested NER is
viewed as a sequence-to-sequence problem, in
which the input sequence consists of the tokens and output sequence of the labels, using
hard attention on the word whose label is being predicted. The proposed methods outperform the nested NER state of the art on four
corpora: ACE-2004, ACE-2005, GENIA and
Czech CNEC. We also enrich our architectures
with the recently published contextual embeddings: ELMo, BERT and Flair, reaching further improvements for the four nested entity
corpora. In addition, we report flat NER stateof-the-art results for CoNLL-2002 Dutch and
Spanish and for CoNLL-2003 English.