The goal of the ScholarPhi project was to improve the reading of scientific papers by helping readers see the meanings of mathematical symbols, technical terms, and other information directly where they are used within the paper.
The ScholarPhi project was led by researchers from UC Berkeley, the Allen Institute for AI (AI2), and the University of Washington. The project was supported by AI2 and the Alfred P. Sloan Foundation.
Demo, Software, and Impact
ScholarPhi research results have had real world impact.
- Try out the research demo.
- Try out the open source code.
- See the research living on as the starting point for AI2's Semantic Reader Project.
ScholarPhi Publications
Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols
ACM CHI Conference on Human Factors in Computing Systems 2021
Math Augmentation: How Authors Enhance the Readability of Formulas using Novel Visual Design Practices
ACM CHI Conference on Human Factors in Computing Systems 2022
Best Paper AwardDocument-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions
EMNLP First Workshop on Scholarly Document Processing 2020
Publications that Make Use of the ScholarPhi Reader
CiteRead: Integrating Localized Citation Contexts into Scientific Paper Reading
ACM Conference on Intelligent User Interfaces 2022
Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing
arXiv pre-print 2203.00130 (2022)
Scim: Intelligent Faceted Highlights for Interactive, Multi-Pass Skimming of Scientific Papers
arXiv pre-print 2205.04561 (2022)
Core Team
UC Berkeley, now Assistant Professor at U Minnesota
UC Berkeley, now Assistant Professor at U Pennsylvania
