  This paper focuses on the study of recognizing discontiguous entities.
Motivated by a previous work, we propose to use a novel hypergraph
representation to jointly encode discontiguous entities of unbounded length,
which can overlap with one another. To compare with existing approaches, we
first formally introduce the notion of model ambiguity, which defines the
difficulty level of interpreting the outputs of a model, and then formally
analyze the theoretical advantages of our model over previous existing
approaches based on linear-chain CRFs. Our empirical results also show that our
model is able to achieve significantly better results when evaluated on
standard data with many discontiguous entities.
