Entity matching with transformer architectures - a step forward in data integration

Transformer architectures have proven to be very effective and provide state-of-the-art results in many natural language tasks. The attention-based architecture in combination with pre-training on large amounts of text lead to the recent breakthrough and a variety of slightly different implementations. In this paper we analyze how well four of the most recent attention-based transformer architectures (BERT, XLNet, RoBERTa and DistilBERT) perform on the task of entity matching - a crucial part of data integration. Entity matching (EM) is the task of finding data instances that refer to the same real-world entity. It is a challenging task if the data instances consist of long textual data or if the data instances are "dirty" due to misplaced values. To evaluate the capability of transformer architectures and transfer-learning on the task of EM, we empirically compare the four approaches on inherently difficult data sets. We show that transformer architectures outperform classical deep learning methods in EM by an average margin of 27.5%

Focus on Quality: Communication in the Health Care Encounter

Outlines findings from focus groups on the role of effective communication between physicians and patients in improving the quality of health care and outcomes. Analyzes responses by race/ethnicity and gender. Includes recommendations

Nordseemodell - Eine neue Grundlage für die Regulierung der Fischerei

Vortrag, gehalten auf der 16. Nordischen Fischereikonferenz, Mariehamn, Aaland, 28. - 31. 8.1978 von ERIK URSIN, Danmarks Fiskeri-og Havundersoegelser, Charlottenlun

The Missing Normative Dimension in Brian Leiter\u27s Reconstructed Legal Realism

Legal Realism has undergone a revitalization in academia. In a series of articles over the past decade and a half, and in a 2007 book, Brian Leiter has offered a philosophical reconstruction of Legal Realism... In the forthcoming Article, I will seek to clarify further the normative dimension of Legal Realism. I will suggest that it is a mistake to divide Legal Realists into quietist camps. This is because these terms refer to two distinct phenomena. Nonquetism in a view of the lawmaking role: judges are legislators-they make law and policy plays a role in their lawmaking. Quietism reflects a conclusion: it makes no sense to give normative advice. In the present Article, I have continued to use these terms as Leiter uses them so as not to confuse the analysis and because in the context of this Article they prove adequate