https://www.semanticscholar.org/product/semantic-reader Semantic ScholarSemantic Scholar [ ][Search] Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. [60908306c5] Icon - Research Dashboard Research Research DashboardResearch FeedsLibrary Icon - Account Account SettingsSign Out Menu [60908306c5] About About UsPublisher PartnersData Partners Research TeamProjectsPublications Resources TutorialsFAQsFor LibrariansAPICORD-19S2ORCBeta Program Sign Up Menu Illustration: Semantic Reader example showing how citations can be viewed in context of the rest of the paper. Logo: Semantic Reader Semantic Reader Introducing Semantic Reader An AI-Powered Augmented Scientific Reading Application, Now Available in Beta. Background Semantic Reader Beta is an augmented reader with the potential to revolutionize scientific reading by making it more accessible and richly contextual. Observations of scientists reading technical papers showed that readers frequently page back and forth looking for the definitions of terms and mathematical symbols as well as for the details of cited papers. This need to jump around through the paper breaks the flow of paper comprehension. Semantic Reader provides this information directly in context by dimming unrelated text and providing details in tooltips, and soon will also provide corresponding term definitions. It uses artificial intelligence to understand a document's structure. Usability studies show readers answered questions requiring deep understanding of paper concepts significantly more quickly with ScholarPhi than with a baseline PDF reader; furthermore, they viewed much less of the paper. Based on the ScholarPhi research from the Semantic Scholar team at AI2, UC Berkeley and the University of Washington, and supported in part by the Alfred P. Sloan Foundation, the Semantic Reader is now available in beta for a select group of arXiv papers on semanticscholar.org with plans to add additional features and expand coverage soon. Illustration: Going back and forth between pages in a research paper Paper Examples Here are examples of Semantic Reader operating over popular Computer Science papers across various fields. We are incrementally improving, testing, and rolling out features from ScholarPhi in Semantic Reader so stay tuned. * NLP Natural Language Processing * Deep Speech 2: End-to-End Speech Recognition in English and Mandarin * An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling * ALBERT: A Lite BERT for Self-supervised Learning of Language Representations * Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation * CV Computer Vision * Long-term Recurrent Convolutional Networks for Visual Recognition and Description * Image-to-Image Translation with Conditional Adversarial Networks * Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution * Rethinking the Inception Architecture for Computer Vision * ML Machine Learning * Conditional Generative Adversarial Nets * Learning Important Features Through Propagating Activation Differences * WaveNet: A Generative Model for Raw Audio * Pixel Recurrent Neural Networks Send us your Semantic Reader feedback. Powered by State-of-the-Art Research Semantic Reader is based on research from the Semantic Scholar team at AI2, UC Berkeley and the University of Washington, and supported in part by the Alfred P. Sloan Foundation. Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols * + Andrew Head, + Kyle Lo, + Dongyeop Kang, + Raymond Fok, + Sam Skjonsberg, + Daniel S. Weld, + Marti A. Hearst * ACM CHI Conference on Human Factors in Computing Systems * * Published 2020 Despite the central importance of research papers to scientific progress, they can be difficult to read. Comprehension is often stymied when the information needed to understand a passage resides somewhere else: in another section, or in another paper. In this work, we envision... Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions * + Dongyeop Kang, + Andrew Head, + Risham Dishu, + Kyle Lo, + Daniel S. Weld, + Marti A. Hearst * EMNLP First Workshop on Scholarly Document Processing * * Published 2020 The task of definition detection is important for scholarly papers, because papers often make use of technical terminology that may be unfamiliar to readers. Despite prior work on definition detection, current approaches are far from being accurate enough to use... Experience a smarter way to search and discover scholarly research. Create Your Account Latest News & Updates Introducing TLDRs on Semantic Scholar Introducing TLDRs on Semantic Scholar Nov 16, 2020 3 min read Semantic Scholar's new AI-powered TLDR feature, now available in beta mode, automatically generates extreme summaries to help you decide which papers are most relevant to your work. Semantic Scholar Semantic Scholar CORD-19 One Year Later CORD-19 One Year Later Mar 16, 2021 6 min read Looking back over a year of impact with co-creators Lucy Lu Wang and Kyle Lo Semantic Scholar Semantic Scholar Breaking down paywalls and building bridges across disciplines with open access Breaking down paywalls and building bridges across disciplines with open access Mar 19, 2021 3 min read Sebastian Kohlmeier is the Head of Operations for Semantic Scholar at the Allen Institute for AI, and Product Manager for CORD-19. Dr... Semantic Scholar Semantic Scholar Stay Connected With Semantic Scholar What Is Semantic Scholar? Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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