# Fast End-to-End Wikification

arXiv: https://arxiv.org/abs/1908.06785 · HTML: https://mlsanity.com/paper/1908.06785/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres](https://mlsanity.com/paper/2109.07449/) (2021)
- [Project Debater APIs: Decomposing the AI Grand Challenge](https://mlsanity.com/paper/2110.01029/) (2021)
- [COFAR: Commonsense and Factual Reasoning in Image Search](https://mlsanity.com/paper/2210.08554/) (2022)

## Abstract (arXiv)

Wikification of large corpora is beneficial for various NLP applications. Existing methods focus on quality performance rather than run-time, and are therefore non-feasible for large data. Here, we introduce RedW, a run-time oriented Wikification solution, based on Wikipedia redirects, that can Wikify massive corpora with competitive performance. We further propose an efficient method for estimating RedW confidence, opening the door for applying more demanding methods only on top of RedW lower-confidence results. Our experimental results support the validity of the proposed approach.