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

arXiv: https://arxiv.org/abs/1609.08075 · HTML: https://mlsanity.com/paper/1609.08075/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2016.

## Built on by (most cited first, 16 of 16)

- [Complex Knowledge Base Question Answering: A Survey](https://mlsanity.com/paper/2108.06688/) (2021)
- [Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base](https://mlsanity.com/paper/2109.03614/) (2021)
- [Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking](https://mlsanity.com/paper/2101.02394/) (2021)
- [Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases](https://mlsanity.com/paper/2108.08597/) (2021)
- [Survey on English Entity Linking on Wikidata](https://mlsanity.com/paper/2112.01989/) (2021)
- [TweetNERD -- End to End Entity Linking Benchmark for Tweets](https://mlsanity.com/paper/2210.08129/) (2022)
- [Modeling Global Semantics for Question Answering over Knowledge Bases](https://mlsanity.com/paper/2101.01510/) (2021)
- [Namesakes: Ambiguously Named Entities from Wikipedia and News](https://mlsanity.com/paper/2111.11372/) (2021)
- [Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension](https://mlsanity.com/paper/2302.13241/) (2023)
- [Semantic Parsing for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2401.06772/) (2023)
- [Exploiting Rich Syntax for Better Knowledge Base Question Answering](https://mlsanity.com/paper/2107.07940/) (2021)
- [Recent Advances in Automated Question Answering In Biomedical Domain](https://mlsanity.com/paper/2111.05937/) (2021)
- [Better Query Graph Selection for Knowledge Base Question Answering](https://mlsanity.com/paper/2204.12662/) (2022)
- [A Method of Query Graph Reranking for Knowledge Base Question Answering](https://mlsanity.com/paper/2204.12808/) (2022)
- [Named Entity Linking with Entity Representation by Multiple Embeddings](https://mlsanity.com/paper/2205.10498/) (2022)
- [CLIMATELI: Evaluating Entity Linking on Climate Change Data](https://mlsanity.com/paper/2406.16732/) (2024)

## Abstract (arXiv)

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.