From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction

1 code implementation • LREC 2022 • Robert Vacareanu, Marco A. Valenzuela-Escarcega, George C. G. Barbosa, Rebecca Sharp, Mihai Surdeanu

While deep learning approaches to information extraction have had many successes, they can be difficult to augment or maintain as needs shift.

Enumerative Search Few-Shot Learning +1

AutoMATES: Automated Model Assembly from Text, Equations, and Software

1 code implementation • 21 Jan 2020 • Adarsh Pyarelal, Marco A. Valenzuela-Escarcega, Rebecca Sharp, Paul D. Hein, Jon Stephens, Pratik Bhandari, HeuiChan Lim, Saumya Debray, Clayton T. Morrison

Models of complicated systems can be represented in different ways - in scientific papers, they are represented using natural language text as well as equations.

Learning what to read: Focused machine reading

no code implementations • EMNLP 2017 • Enrique Noriega-Atala, Marco A. Valenzuela-Escarcega, Clayton T. Morrison, Mihai Surdeanu

In this work, we introduce a focused reading approach to guide the machine reading of biomedical literature towards what literature should be read to answer a biomedical query as efficiently as possible.

Reading Comprehension Reinforcement Learning (RL)

SnapToGrid: From Statistical to Interpretable Models for Biomedical Information Extraction

no code implementations • WS 2016 • Marco A. Valenzuela-Escarcega, Gus Hahn-Powell, Dane Bell, Mihai Surdeanu

We propose an approach for biomedical information extraction that marries the advantages of machine learning models, e. g., learning directly from data, with the benefits of rule-based approaches, e. g., interpretability.

BIG-bench Machine Learning Event Extraction