Named Entity Recognition as Dependency Parsing
Abstract
Named Entity Recognition (NER) is a fundamental task in Natural Language Processing,
concerned with identifying spans of text expressing references to entities. NER research
is often focused on flat entities only (flat NER),
ignoring the fact that entity references can be
nested, as in [Bank of [China]] (Finkel and
Manning, 2009). In this paper, we use ideas
from graph-based dependency parsing to provide our model a global view on the input via
a biaffine model (Dozat and Manning, 2017).
The biaffine model scores pairs of start and end
tokens in a sentence which we use to explore
all spans, so that the model is able to predict
named entities accurately. We show that the
model works well for both nested and flat NER
through evaluation on 8 corpora and achieving
SoTA performance on all of them, with accuracy gains of up to 2.2 percentage points.
concerned with identifying spans of text expressing references to entities. NER research
is often focused on flat entities only (flat NER),
ignoring the fact that entity references can be
nested, as in [Bank of [China]] (Finkel and
Manning, 2009). In this paper, we use ideas
from graph-based dependency parsing to provide our model a global view on the input via
a biaffine model (Dozat and Manning, 2017).
The biaffine model scores pairs of start and end
tokens in a sentence which we use to explore
all spans, so that the model is able to predict
named entities accurately. We show that the
model works well for both nested and flat NER
through evaluation on 8 corpora and achieving
SoTA performance on all of them, with accuracy gains of up to 2.2 percentage points.
Research Areas
