S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking
This addresses tweet information extraction for social media analysis, but it is incremental as it adapts existing tree-based methods to a structured learning context.
The paper tackles the problem of tweet entity linking by proposing S-MART, a tree-based structured learning framework, and shows that it significantly outperforms state-of-the-art systems.
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.