# Entity Matching by Pool-based Active Learning

arXiv: https://arxiv.org/abs/2211.00311 · HTML: https://mlsanity.com/paper/2211.00311/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 50 references that are in the map.
- First posted on arXiv 1 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [A Decomposable Attention Model for Natural Language Inference](https://mlsanity.com/paper/1606.01933/) (2016)
- [A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1910.02923/) (2019)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [Neural Networks for Entity Matching: A Survey](https://mlsanity.com/paper/2010.11075/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Markovian Formalism for Active Querying](https://mlsanity.com/paper/2306.08001/) (2023)

## Abstract (arXiv)

The goal of entity matching is to find the corresponding records representing the same real-world entity from different data sources. At present, in the mainstream methods, rule-based entity matching methods need tremendous domain knowledge. The machine-learning based or deep-learning based entity matching methods need a large number of labeled samples to build the model, which is difficult to achieve in some applications. In addition, learning-based methods are easy to over-fitting, so the quality requirements of training samples are very high. In this paper, we present an active learning method ALMatcher for the entity matching tasks. This method needs to manually label only a small number of valuable samples, and use these samples to build a model with high quality. This paper proposes a hybrid uncertainty as query strategy to find those valuable samples for labeling, which can minimize the number of labeled training samples meanwhile meet the task requirements. The proposed method has been validated on seven data sets in different fields. The experiment shows that ALMatcher uses only a small number of labeled samples and achieves better results compared to existing approaches.