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   Jin, Di ; Sisman, Bunyamin ; Wei, Hao ; Dong, Xin Luna ; Koutra, Danai ( November 2021 , Proceedings of the VLDB Endowment)

   Multi-source entity linkage focuses on integrating knowledge from multiple sources by linking the records that represent the same real world entity. This is critical in high-impact applications such as data cleaning and user stitching. The state-of-the-art entity linkage pipelines mainly depend on supervised learning that requires abundant amounts of training data. However, collecting well-labeled training data becomes expensive when the data from many sources arrives incrementally over time. Moreover, the trained models can easily overfit to specific data sources, and thus fail to generalize to new sources due to significant differences in data and label distributions. To address these challenges, we present AdaMEL, a deep transfer learning framework that learns generic high-level knowledge to perform multi-source entity linkage. AdaMEL models the attribute importance that is used to match entities through an attribute-level self-attention mechanism, and leverages the massive unlabeled data from new data sources through domain adaptation to make it generic and data-source agnostic. In addition, AdaMEL is capable of incorporating an additional set of labeled data to more accurately integrate data sources with different attribute importance. Extensive experiments show that our framework achieves state-of-the-art results with 8.21% improvement on average over methods based on supervised learning. Besides, it is more stable in handling different sets of data sources in less runtime. 

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2. Simultaneous record linkage and causal inference with propensity score subclassification: Simultaneous Record Linkage and Causal Inference

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3. STUDIES OF LINKAGE IN POPULATIONS. VII. TEMPORAL VARIATION AND X-CHROMOSOMAL LINKAGE DISEQUILIBRIUMS

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   Levitan, Max ( September 1973 , Evolution)
4. Injecting Entity Types into Entity-Guided Text Generation

   https://doi.org/10.18653/v1/2021.emnlp-main.56  

   Dong, Xiangyu ; Yu, Wenhao ; Zhu, Chenguang ; Jiang, Meng ( January 2021 , Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing)

   Recent successes in deep generative modeling have led to significant advances in natural language generation (NLG). Incorporating entities into neural generation models has demonstrated great improvements by assisting to infer the summary topic and to generate coherent content. To enhance the role of entity in NLG, in this paper, we aim to model the entity type in the decoding phase to generate contextual words accurately. We develop a novel NLG model to produce a target sequence based on a given list of entities. Our model has a multi-step decoder that injects the entity types into the process of entity mention generation. Experiments on two public news datasets demonstrate type injection performs better than existing type embedding concatenation baselines. 

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5. BERT-ER: Query-specific BERT Entity Representations for Entity Ranking

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   Chatterjee, Shubham ; Dietz, Laura ( July 2022 , Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval)