Non-linear models recently receive a lot of attention as people are starting
to discover the power of statistical and embedding features. However,
tree-based models are seldom studied in the context of structured learning
despite their recent success on various classification and ranking tasks. In
this paper, we propose S-MART, a tree-based structured learning framework based
on multiple additive regression trees. S-MART is especially suitable for
handling tasks with dense features, and can be used to learn many different
structures under various loss functions.
We apply S-MART to the task of tweet entity linking --- a core component of
tweet information extraction, which aims to identify and link name mentions to
entities in a knowledge base. A novel inference algorithm is proposed to handle
the special structure of the task. The experimental results show that S-MART
significantly outperforms state-of-the-art tweet entity linking systems.

本文提出了 S-MART，这是一种基于多个添加回归树的基于树的结构学习框架，特别适用于处理密集特征的任务，并可用于在各种损失函数下学习许多不同的结构。我们将 S-MART 应用于推文实体链接任务，并提出了一种新颖的推断算法来处理任务的特殊结构，实验结果表明，S-MART 显著优于最先进的推文实体链接系统。