5 citations · 14 across the 6 of their papers we have counts for
6 papers
From Unstructured Text to Causal Knowledge Graphs: A Transformer-Based Approach
Scott Friedman, Ian Magnusson, Vasanth Sarathy +1
Qualitative causal relationships compactly express the direction, dependency, temporal constraints, and monotonicity constraints of discrete or continuous interactions in the world…
Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results
Ian H. Magnusson, Scott E. Friedman
Recent transformer-based approaches demonstrate promising results on relational scientific information extraction. Existing datasets focus on high-level description of how research…
Extracting Qualitative Causal Structure with Transformer-Based NLP
Scott E. Friedman, Ian H. Magnusson, Sonja M. Schmer-Galunder
Qualitative causal relationships compactly express the direction, dependency, temporal constraints, and monotonicity constraints of discrete or continuous interactions in the world…
Provenance-Based Interpretation of Multi-Agent Information Analysis
Scott Friedman, Jeff Rye, David LaVergne +3
Analytic software tools and workflows are increasing in capability, complexity, number, and scale, and the integrity of our workflows is as important as ever. Specifically, we must…
Provenance-Based Assessment of Plans in Context
Scott E. Friedman, Robert P. Goldman, Richard G. Freedman +3
Many real-world planning domains involve diverse information sources, external entities, and variable-reliability agents, all of which may impact the confidence, risk, and sensitiv…
Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise
Nikhil Krishnaswamy, Scott Friedman, James Pustejovsky
Many modern machine learning approaches require vast amounts of training data to learn new concepts; conversely, human learning often requires few examples--sometimes only one--fro…