Low-resource Deep Entity Resolution with Transfer and Active Learning

Kasai, Jungo; Qian, Kun; Gurajada, Sairam; Li, Yunyao; Popa, Lucian

doi:10.48550/arxiv.1906.08042

Cited by 14 publications

(12 citation statements)

References 26 publications

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“…DeepMatcher (DM) is a state-of-the-art deep learning based entity matching method for ER [27]. Deep Transfer active learning (DTAL) is the state-of-the-art active learning method which combines both transfer learning and active learning for handling ER tasks [21].…”

Section: Methodsmentioning

confidence: 99%

ErGAN: Generative Adversarial Networks for Entity Resolution

Shao

Wang

Wijesinghe

et al. 2020

Preprint

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Entity resolution targets at identifying records that represent the same real-world entity from one or more datasets. A major challenge in learning-based entity resolution is how to reduce the label cost for training. Due to the quadratic nature of record pair comparison, labeling is a costly task that requires a significant effort from human experts. However, without sufficient training data, a powerful machine learning model may be overfitting. This challenge is further aggravated when the underlying data distribution is highly imbalanced, which commonly occurs in entity resolution applications. Inspired by recent advances of generative adversarial network (GAN), in this paper, we propose a novel deep learning method, called ERGAN, to address the challenge. ERGAN consists of two key components: a label generator and a discriminator which are optimized alternatively through adversarial learning. To alleviate the issues of overfitting and highly imbalanced distribution, we design two novel modules for diversity and propagation, which can greatly improve the model generalization power. We theoretically prove that ERGAN can overcome the model collapse and convergence problems in the original GAN. We also conduct extensive experiments to empirically verify the labeling and learning efficiency of ERGAN. The experimental results show that ERGAN beats all state-ofthe-art baselines, including unsupervised, semi-supervised, and unsupervised learning methods.

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Section: Methodsmentioning

confidence: 99%

ErGAN: Generative Adversarial Networks for Entity Resolution

Shao

Wang

Wijesinghe

et al. 2020

Preprint

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“…Data Resource For high-resource languages, the annotated data can be of different sizes; for low-resource languages, large amounts of data do not often exist (Kasai et al, 2019). We explore the effects of different data sizes when training and testing LIMs.…”

Section: Factor Characterizationmentioning

confidence: 99%

An Empirical Study of Factors Affecting Language-Independent Models

Liu¹,

Tong²,

Xu³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Scaling existing applications and solutions to multiple human languages has traditionally proven to be difficult, mainly due to the language-dependent nature of preprocessing and feature engineering techniques employed in traditional approaches. In this work, we empirically investigate the factors affecting language-independent models built with multilingual representations, including task type, language set and data resource.On two most representative Natural Language Processing taskssentence classification and sequence labeling, we show that language-independent models can be comparable to or even outperforms the models trained using monolingual data, and they are generally more effective on sentence classification. We experiment language-independent models with many different languages and show that they are more suitable for typologically similar languages. We also explore the effects of different data sizes when training and testing language-independent models, and demonstrate that they are not only suitable for high-resource languages, but also very effective in low-resource languages.

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“…Entity matching (EM) [16], which is to identify data instances that refer to the same real-world entity, is also related. Some EM works also employ a deep learning-based approach [24], [37], [42], [49], [57], [73], [82]. Mudgal and et al [57] evaluates and compares the performance of different deep learning models applied to EM with three types of data: structured data, textual data, and dirty data (with missing value, inconsistent attributes and/or miss-placed values).…”

Section: Schema/entity Matchingmentioning

confidence: 99%

“…In the past few years, deep learning (DL) has become the most popular direction in machine learning and artificial intelligence [46], [65], and has transformed a lot of research areas, such as image recognition, computer vision, speech recognition, natural language processing, etc.. In recent years, DL has been applied to database systems and applications to facilitate parameter tuning [47], [71], [76], [81], indexing [21], [43], partitioning [34], [86], cardinality estimation and query optimization [39], [44], and entity matching [24], [37], [42], [57], [73], [82]. While predictions based on deep learning cannot guarantee correctness, in the Big Data era, errors in data integration are usually tolerable as long as most of the data is correct, which is another motivation of our work.…”

Section: Introductionmentioning

confidence: 99%

Survive the Schema Changes: Integration of Unmanaged Data Using Deep Learning

Wang,

Zhou,

Das

et al. 2020

Preprint

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Data is the king in the age of AI. However data integration is often a laborious task that is hard to automate. Schema change is one significant obstacle to the automation of the end-to-end data integration process. Although there exist mechanisms such as query discovery and schema modification language to handle the problem, these approaches can only work with the assumption that the schema is maintained by a database. However, we observe diversified schema changes in heterogeneous data and open data, most of which has no schema defined. In this work, we propose to use deep learning to automatically deal with schema changes through a super cell representation and automatic injection of perturbations to the training data to make the model robust to schema changes. Our experimental results demonstrate that our proposed approach is effective for two realworld data integration scenarios: coronavirus data integration, and machine log integration.

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Low-resource Deep Entity Resolution with Transfer and Active Learning

Cited by 14 publications

References 26 publications

ErGAN: Generative Adversarial Networks for Entity Resolution

ErGAN: Generative Adversarial Networks for Entity Resolution

An Empirical Study of Factors Affecting Language-Independent Models

Survive the Schema Changes: Integration of Unmanaged Data Using Deep Learning

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