# CollaborER: A Self-supervised Entity Resolution Framework Using Multi-features Collaboration

arXiv: https://arxiv.org/abs/2108.08090 · HTML: https://mlsanity.com/paper/2108.08090/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2021.

## Built on by (most cited first, 2 of 2)

- [Entity Matching using Large Language Models](https://mlsanity.com/paper/2310.11244/) (2023)
- [WDC Products: A Multi-Dimensional Entity Matching Benchmark](https://mlsanity.com/paper/2301.09521/) (2023)

## Abstract (arXiv)

Entity Resolution (ER) aims to identify whether two tuples refer to the same real-world entity and is well-known to be labor-intensive. It is a prerequisite to anomaly detection, as comparing the attribute values of two matched tuples from two different datasets provides one effective way to detect anomalies. Existing ER approaches, due to insufficient feature discovery or error-prone inherent characteristics, are not able to achieve stable performance. In this paper, we present CollaborER, a self-supervised entity resolution framework via multi-features collaboration. It is capable of (i) obtaining reliable ER results with zero human annotations and (ii) discovering adequate tuples' features in a fault-tolerant manner. CollaborER consists of two phases, i.e., automatic label generation (ALG) and collaborative ER training (CERT). In the first phase, ALG is proposed to generate a set of positive tuple pairs and a set of negative tuple pairs. ALG guarantees the high quality of the generated tuples and hence ensures the training quality of the subsequent CERT. In the second phase, CERT is introduced to learn the matching signals by discovering graph features and sentence features of tuples collaboratively. Extensive experimental results over eight real-world ER benchmarks show that CollaborER outperforms all the existing unsupervised ER approaches and is comparable or even superior to the state-of-the-art supervised ER methods.