Illustration: Semantic Reader example showing how citations can be viewed in context of the rest of the paper.
Logo: 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

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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

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

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...

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Latest News & Updates

Featured AI2er: Bailey Kuehl

Featured AI2er: Bailey Kuehl

Bailey Kuehl is a Data Science Analyst on the Semantic Scholar team at AI2.

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