  In medical documents, it is possible that an entity of interest not only
contains a discontiguous sequence of words but also overlaps with another
entity. Entities of such structures are intrinsically hard to recognize due to
the large space of possible entity combinations. In this work, we propose a
neural two-stage approach to recognize discontiguous and overlapping entities
by decomposing this problem into two subtasks: 1) it first detects all the
overlapping spans that either form entities on their own or present as segments
of discontiguous entities, based on the representation of segmental hypergraph,
2) next it learns to combine these segments into discontiguous entities with a
classifier, which filters out other incorrect combinations of segments. Two
neural components are designed for these subtasks respectively and they are
learned jointly using a shared encoder for text. Our model achieves the
state-of-the-art performance in a standard dataset, even in the absence of
external features that previous methods used.
