  When an entity name contains other names within it, the identification of all
combinations of names can become difficult and expensive. We propose a new
method to recognize not only outermost named entities but also inner nested
ones. We design an objective function for training a neural model that treats
the tag sequence for nested entities as the second best path within the span of
their parent entity. In addition, we provide the decoding method for inference
that extracts entities iteratively from outermost ones to inner ones in an
outside-to-inside way. Our method has no additional hyperparameters to the
conditional random field based model widely used for flat named entity
recognition tasks. Experiments demonstrate that our method performs better than
or at least as well as existing methods capable of handling nested entities,
achieving the F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE-2005,
and GENIA datasets, respectively.
