  It is common that entity mentions can contain other mentions recursively.
This paper introduces a scalable transition-based method to model the nested
structure of mentions. We first map a sentence with nested mentions to a
designated forest where each mention corresponds to a constituent of the
forest. Our shift-reduce based system then learns to construct the forest
structure in a bottom-up manner through an action sequence whose maximal length
is guaranteed to be three times of the sentence length. Based on Stack-LSTM
which is employed to efficiently and effectively represent the states of the
system in a continuous space, our system is further incorporated with a
character-based component to capture letter-level patterns. Our model achieves
the state-of-the-art results on ACE datasets, showing its effectiveness in
detecting nested mentions.
