# EmbedJoin: Efficient Edit Similarity Joins via Embeddings

arXiv: https://arxiv.org/abs/1702.00093 · HTML: https://mlsanity.com/paper/1702.00093/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2017.

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

- [Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage](https://mlsanity.com/paper/2207.04684/) (2022)
- [Domain specificity and data efficiency in typo tolerant spell checkers: the case of search in online marketplaces](https://mlsanity.com/paper/2308.01976/) (2023)
- [Xling: A Learned Filter Framework for Accelerating High-Dimensional Approximate Similarity Join](https://mlsanity.com/paper/2402.13397/) (2024)

## Abstract (arXiv)

We study the problem of edit similarity joins, where given a set of strings and a threshold value K, we want to output all pairs of strings whose edit distances are at most K. Edit similarity join is a fundamental problem in data cleaning/integration, bioinformatics, collaborative filtering and natural language processing, and has been identified as a primitive operator for database systems. This problem has been studied extensively in the literature. However, we have observed that all the existing algorithms fall short on long strings and large distance thresholds. In this paper we propose an algorithm named EmbedJoin which scales very well with string length and distance threshold. Our algorithm is built on the recent advance of metric embeddings for edit distance, and is very different from all of the previous approaches. We demonstrate via an extensive set of experiments that EmbedJoin significantly outperforms the previous best algorithms on long strings and large distance thresholds.