# Low-resource Deep Entity Resolution with Transfer and Active Learning

arXiv: https://arxiv.org/abs/1906.08042 · HTML: https://mlsanity.com/paper/1906.08042/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

## Built on by (most cited first, 30 of 37)

- [Selective Annotation Makes Language Models Better Few-Shot Learners](https://mlsanity.com/paper/2209.01975/) (2022)
- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- [On the Importance of Effectively Adapting Pretrained Language Models for Active Learning](https://mlsanity.com/paper/2104.08320/) (2021)
- [AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark](https://mlsanity.com/paper/2412.06724/) (2024)
- [Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark]](https://mlsanity.com/paper/2304.12329/) (2023)
- [Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks](https://mlsanity.com/paper/2311.09825/) (2023)
- [Learning to Parallelize in a Shared-Memory Environment with Transformers](https://mlsanity.com/paper/2204.12835/) (2022)
- [Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture](https://mlsanity.com/paper/2305.12710/) (2023)
- [Deep Indexed Active Learning for Matching Heterogeneous Entity Representations](https://mlsanity.com/paper/2104.03986/) (2021)
- [DAME: Domain Adaptation for Matching Entities](https://mlsanity.com/paper/2204.09244/) (2022)
- [On the Limitations of Simulating Active Learning](https://mlsanity.com/paper/2305.13342/) (2023)
- [Data Cleaning and Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2310.01765/) (2023)
- [KcMF: A Knowledge-compliant Framework for Schema and Entity Matching with Fine-tuning-free LLMs](https://mlsanity.com/paper/2410.12480/) (2024)
- [Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation](https://mlsanity.com/paper/2110.14509/) (2021)
- [Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions](https://mlsanity.com/paper/2211.11798/) (2022)
- [Deep Active Alignment of Knowledge Graph Entities and Schemata](https://mlsanity.com/paper/2304.04389/) (2023)
- [Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction](https://mlsanity.com/paper/2402.13741/) (2024)
- [Improving Question Answering Performance Using Knowledge Distillation and Active Learning](https://mlsanity.com/paper/2109.12662/) (2021)
- [FlexER: Flexible Entity Resolution for Multiple Intents](https://mlsanity.com/paper/2209.07569/) (2022)
- [Learning from Natural Language Explanations for Generalizable Entity Matching](https://mlsanity.com/paper/2406.09330/) (2024)
- [Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysis](https://mlsanity.com/paper/2212.11680/) (2022)
- [A Critical Re-evaluation of Benchmark Datasets for (Deep) Learning-Based Matching Algorithms](https://mlsanity.com/paper/2307.01231/) (2023)
- [Cost-Efficient Prompt Engineering for Unsupervised Entity Resolution](https://mlsanity.com/paper/2310.06174/) (2023)
- [The Battleship Approach to the Low Resource Entity Matching Problem](https://mlsanity.com/paper/2311.15685/) (2023)
- [Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making](https://mlsanity.com/paper/2106.04174/) (2021)
- [HumanAL: Calibrating Human Matching Beyond a Single Task](https://mlsanity.com/paper/2205.03209/) (2022)
- [MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching](https://mlsanity.com/paper/2308.01927/) (2023)
- [Privacy-preserving Deep Learning based Record Linkage](https://mlsanity.com/paper/2211.02161/) (2022)
- [A Primer on the Data Cleaning Pipeline](https://mlsanity.com/paper/2307.13219/) (2023)

## Abstract (arXiv)

Entity resolution (ER) is the task of identifying different representations of the same real-world entities across databases. It is a key step for knowledge base creation and text mining. Recent adaptation of deep learning methods for ER mitigates the need for dataset-specific feature engineering by constructing distributed representations of entity records. While these methods achieve state-of-the-art performance over benchmark data, they require large amounts of labeled data, which are typically unavailable in realistic ER applications. In this paper, we develop a deep learning-based method that targets low-resource settings for ER through a novel combination of transfer learning and active learning. We design an architecture that allows us to learn a transferable model from a high-resource setting to a low-resource one. To further adapt to the target dataset, we incorporate active learning that carefully selects a few informative examples to fine-tune the transferred model. Empirical evaluation demonstrates that our method achieves comparable, if not better, performance compared to state-of-the-art learning-based methods while using an order of magnitude fewer labels.