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arXiv · 1810.01808

A Neural Transition-based Model for Nested Mention Recognition

Bailin Wang·Wei Lu·Yu Wang·Hongxia Jin
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Abstract

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.

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Bailin Wang, Wei Lu, Yu Wang, Hongxia Jin. 2018-10-03. A Neural Transition-based Model for Nested Mention Recognition. https://arxiv.org/abs/1810.01808

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