Joint Neural Entity Disambiguation with Output Space Search

Hamed Shahbazi, Xiaoli Fern, Reza Ghaeini, Chao Ma, Rasha Mohammad Obeidat, Prasad Tadepalli


Abstract
In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS). Given an input document, we start from a complete solution constructed by a local model and conduct a search in the space of possible corrections to improve the local solution from a global view point. Our search utilizes a heuristic function to focus more on the least confident local decisions and a pruning function to score the global solutions based on their local fitness and the global coherences among the predicted entities. Experimental results on CoNLL 2003 and TAC 2010 benchmarks verify the effectiveness of our model.
Anthology ID:
C18-1184
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2170–2180
Language:
URL:
https://aclanthology.org/C18-1184
DOI:
Bibkey:
Cite (ACL):
Hamed Shahbazi, Xiaoli Fern, Reza Ghaeini, Chao Ma, Rasha Mohammad Obeidat, and Prasad Tadepalli. 2018. Joint Neural Entity Disambiguation with Output Space Search. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2170–2180, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Joint Neural Entity Disambiguation with Output Space Search (Shahbazi et al., COLING 2018)
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PDF:
https://aclanthology.org/C18-1184.pdf