We present a gradient-tree-boosting-based structured learning model for
jointly disambiguating named entities in a document. Gradient tree boosting is
a widely used machine learning algorithm that underlies many top-performing
natural language processing systems. Surprisingly, most works limit the use of
gradient tree boosting as a tool for regular classification or regression
problems, despite the structured nature of language. To the best of our
knowledge, our work is the first one that employs the structured gradient tree
boosting (SGTB) algorithm for collective entity disambiguation. By defining
global features over previous disambiguation decisions and jointly modeling
them with local features, our system is able to produce globally optimized
entity assignments for mentions in a document. Exact inference is prohibitively
expensive for our globally normalized model. To solve this problem, we propose
Bidirectional Beam Search with Gold path (BiBSG), an approximate inference
algorithm that is a variant of the standard beam search algorithm. BiBSG makes
use of global information from both past and future to perform better local
search. Experiments on standard benchmark datasets show that SGTB significantly
improves upon published results. Specifically, SGTB outperforms the previous
state-of-the-art neural system by near 1\% absolute accuracy on the popular
AIDA-CoNLL dataset.

本研究提出了一种基于渐进树提升（gradient tree boosting）的结构学习模型，用于在文档中共同消歧命名实体，并通过提出的双向束搜索算法 BiBSG 进行近似推断。在标准基准数据集上的实验表明，该模型显着提高了先前发表结果，特别是在 AIDA-CoNLL 数据集上表现优异。