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arXiv 1609.080752016-09-26cs.CL

S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking

Georgia Institute of Technology（佐治亚理工学院）；Microsoft Research（微软研究院）

Yi Yang, Ming-Wei Chang

备注：: Appeared in ACL 2015 proceedings. This is an updated version. More details available in the pdf file

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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.

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