A Neural Transition-based Model for Nested Mention Recognition

Bailin Wang, Wei Lu, Yu Wang, Hongxia Jin


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 gets the state-of-the-art performances in ACE datasets, showing its effectiveness in detecting nested mentions.
Anthology ID:
D18-1124
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1011–1017
Language:
URL:
https://aclanthology.org/D18-1124
DOI:
10.18653/v1/D18-1124
Bibkey:
Cite (ACL):
Bailin Wang, Wei Lu, Yu Wang, and Hongxia Jin. 2018. A Neural Transition-based Model for Nested Mention Recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1011–1017, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
A Neural Transition-based Model for Nested Mention Recognition (Wang et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1124.pdf
Code
 berlino/nest-trans-em18
Data
ACE 2004ACE 2005GENIANNE