15 citations · 25 across the 6 of their papers we have counts for
10 papers
Bidirectional Small-Granularity Search between Code and Text
Marco A. Valenzuela-Escárcega, Enrique Noriega-Atala, Gus Hahn-Powell +2
We introduce the novel task of bidirectional small-granularity search between code and text, where the queries are small snippets of text or code and the results are also small fra…
From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction
Robert Vacareanu, Marco A. Valenzuela-Escarcega, George C. G. Barbosa +2
While deep learning approaches to information extraction have had many successes, they can be difficult to augment or maintain as needs shift. Rule-based methods, on the other hand…
AutoMATES: Automated Model Assembly from Text, Equations, and Software
Adarsh Pyarelal, Marco A. Valenzuela-Escarcega, Rebecca Sharp +6
Models of complicated systems can be represented in different ways - in scientific papers, they are represented using natural language text as well as equations. But to be of real…
Lightly-supervised Representation Learning with Global Interpretability
Marco A. Valenzuela-Escárcega, Ajay Nagesh, Mihai Surdeanu
We propose a lightly-supervised approach for information extraction, in particular named entity classification, which combines the benefits of traditional bootstrapping, i.e., use…
Text Annotation Graphs: Annotating Complex Natural Language Phenomena
Angus G. Forbes, Kristine Lee, Gus Hahn-Powell +2
This paper introduces a new web-based software tool for annotating text, Text Annotation Graphs, or TAG. It provides functionality for representing complex relationships between wo…
Learning what to read: Focused machine reading
Enrique Noriega-Atala, Marco A. Valenzuela-Escarcega, Clayton T. Morrison +1
Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into…
SnapToGrid: From Statistical to Interpretable Models for Biomedical Information Extraction
Marco A. Valenzuela-Escarcega, Gus Hahn-Powell, Dane Bell +1
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-ba…
This before That: Causal Precedence in the Biomedical Domain
Gus Hahn-Powell, Dane Bell, Marco A. Valenzuela-Escárcega +1
Causal precedence between biochemical interactions is crucial in the biomedical domain, because it transforms collections of individual interactions, e.g., bindings and phosphoryla…
Sieve-based Coreference Resolution in the Biomedical Domain
Dane Bell, Gus Hahn-Powell, Marco A. Valenzuela-Escárcega +1
We describe challenges and advantages unique to coreference resolution in the biomedical domain, and a sieve-based architecture that leverages domain knowledge for both entity and…
Description of the Odin Event Extraction Framework and Rule Language
Marco A. Valenzuela-Escárcega, Gus Hahn-Powell, Mihai Surdeanu
This document describes the Odin framework, which is a domain-independent platform for developing rule-based event extraction models. Odin aims to be powerful (the rule language al…