2019/06/17 by Jungo Kasai, Kasai, Jungo, Kun Qian +7 · 2 citations
Computer Science · Decision Sciences · #Computation and Language (cs.CL) #Data Quality and Management #Databases (cs.DB) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Topic Modeling #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.1906.08042
openalex publication_date 2019/06/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.