This paper shows how to construct knowledge graphs (KGs) from pre-trained language models (e.g., BERT, GPT-2/3), without human supervision. Popular KGs (e.g, Wikidata, NELL) are built in either a supervised or semi-supervised manner, requiring humans to create knowledge. Recent deep language models automatically acquire knowledge from large-scale corpora via pre-training. The stored knowledge has enabled the language models to improve downstream NLP tasks, e.g., answering questions, and writing code and articles. In this paper, we propose an unsupervised method to cast the knowledge contained within language models into KGs. We show that KGs are constructed with a single forward pass of the pre-trained language models (without fine-tuning) over the corpora. We demonstrate the quality of the constructed KGs by comparing to two KGs (Wikidata, TAC KBP) created by humans. Our KGs also provide open factual knowledge that is new in the existing KGs. Our code and KGs will be made publicly available.

Over the past few years, the manufacturing industry continued to remain a critical force in both advanced and developing economies. The sector has gone through significant transformations bringing out new opportunities and challenges to business leaders and policy makers. Get PDF Sample Copy of this report at https://decisionmarketreports.com/request-sample/1247548 In advanced economies, the manufacturing sector has largely concentrated on promoting innovation, productivity and trade more than growth and employment. In many advanced economies manufacturing sector has to consume more services and rely heavily on them to operate.

LEBANON, NH - Recently, deep learning methods have shown promising results for analyzing histological patterns in microscopy images. These approaches, however, require a laborious, high-cost, manual annotation process by pathologists called "region-of-interest annotations." A research team at Dartmouth and Dartmouth-Hitchcock Norris Cotton Cancer Center, led by Saeed Hassanpour, PhD, has addressed this shortcoming of current methods by developing a novel attention-based deep learning method that automatically learns clinically important regions on whole-slide images to classify them. The team tested their new approach for identifying cancerous and precancerous esophagus tissue on high-resolution microscopy images without training on region-of-interest annotations. "Our new approach outperformed the current state-of-the-art approach that requires these detailed annotations for its training," concludes Hassanpour.

Exactly a century ago, a Tennessee entrepreneur named Clarence Saunders was granted a patent for a new idea that would disrupt retail by cutting jobs and costs at the same time. Though Saunders' name isn't well-known, you might have interacted with his invention in the past week or so: the self-service grocery store, where you choose your own items from the shelves. Before Saunders opened the first Piggly Wiggly in Memphis, customers would hand a shopping list to a clerk, who would assemble the order. It's an example of innovation that has endured. But in 2017, a group of entrepreneurs are starting to wonder whether more cost -- and more jobs -- could be wrung from the grocery business by having robots roam the aisles.