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 shiftreduce 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 letterlevel patterns. Our model achieves the stateof-the-art results on ACE datasets, showing its effectiveness in detecting nested mentions.
Based on the same idea, REF proposed a scalable transition-based approach to construct a constituency forest (a collection of constituency trees).
A Neural Transition-based Model for Nested Mention Recognition
ArXiv
ArXiv
Computer Science