Learning from Knowledge Graphs: Neural Fine-Grained Entity Typing with Copy-Generation Networks.

Fine-grained entity typing (FET) aims to identify the semantic type of an entity in a plain text, which is a significant task for downstream natural language processing applications. However, most existing methods neglect rich known typing information about these entities in knowledge graphs. To address this issue, we take advantage of knowledge graphs to improve fine-grained entity typing through the use of a copy mechanism.

Literature study on clinical treatment of facial paralysis in the last 20 years using Web of Science: Comparison between rehabilitation, physiotherapy and acupuncture.

Background: Facial paralysis is defined as severe or complete loss of facial muscle motor function.

Objective: The study was undertaken to explore a bibliometric approach to quantitatively assess the research on clinical treatment of facial paralysis using rehabilitation, physiotherapy and acupuncture using Web of Science from 1992 to 2011.

Design: Bibliometric approach.