@inproceedings{shahbazi-etal-2018-joint,
title = "Joint Neural Entity Disambiguation with Output Space Search",
author = "Shahbazi, Hamed and
Fern, Xiaoli and
Ghaeini, Reza and
Ma, Chao and
Obeidat, Rasha Mohammad and
Tadepalli, Prasad",
editor = "Bender, Emily M. and
Derczynski, Leon and
Isabelle, Pierre",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-1184/",
pages = "2170--2180",
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."
}
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%0 Conference Proceedings
%T Joint Neural Entity Disambiguation with Output Space Search
%A Shahbazi, Hamed
%A Fern, Xiaoli
%A Ghaeini, Reza
%A Ma, Chao
%A Obeidat, Rasha Mohammad
%A Tadepalli, Prasad
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F shahbazi-etal-2018-joint
%X 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.
%U https://aclanthology.org/C18-1184/
%P 2170-2180
Markdown (Informal)
[Joint Neural Entity Disambiguation with Output Space Search](https://aclanthology.org/C18-1184/) (Shahbazi et al., COLING 2018)
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.