no code implementations • NAACL (ACL) 2022 • Robert Vacareanu, George C.G. Barbosa, Enrique Noriega-Atala, Gus Hahn-Powell, Rebecca Sharp, Marco A. Valenzuela-Escárcega, Mihai Surdeanu
We propose a system that assists a user in constructing transparent information extraction models, consisting of patterns (or rules) written in a declarative language, through program synthesis. Users of our system can specify their requirements through the use of examples, which are collected with a search interface. The rule-synthesis system proposes rule candidates and the results of applying them on a textual corpus; the user has the option to accept the candidate, request another option, or adjust the examples provided to the system. Through an interactive evaluation, we show that our approach generates high-precision rules even in a 1-shot setting.
no code implementations • PANDL (COLING) 2022 • Enrique Noriega-Atala, Robert Vacareanu, Gus Hahn-Powell, Marco A. Valenzuela-Escárcega
We propose a neural-based approach for rule synthesis designed to help bridge the gap between the interpretability, precision and maintainability exhibited by rule-based information extraction systems with the scalability and convenience of statistical information extraction systems.
no code implementations • WS 2019 • 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 of limited annotations and interpretability of extraction patterns, with the robust learning approaches proposed in representation learning.
1 code implementation • LREC 2018 • Angus G. Forbes, Kristine Lee, Gus Hahn-Powell, Marco A. Valenzuela-Escárcega, Mihai Surdeanu
Additionally, we include an approach to representing text annotations in which annotation subgraphs, or semantic summaries, are used to show relationships outside of the sequential context of the text itself.
2 code implementations • WS 2016 • Gus Hahn-Powell, Dane Bell, Marco A. Valenzuela-Escárcega, Mihai Surdeanu
Causal precedence between biochemical interactions is crucial in the biomedical domain, because it transforms collections of individual interactions, e. g., bindings and phosphorylations, into the causal mechanisms needed to inform meaningful search and inference.
no code implementations • LREC 2016 • Dane Bell, Gus Hahn-Powell, Marco A. Valenzuela-Escárcega, Mihai Surdeanu
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 event coreference resolution.
1 code implementation • 24 Sep 2015 • Marco A. Valenzuela-Escárcega, Gus Hahn-Powell, Mihai Surdeanu
Here we include a thorough definition of the Odin rule language, together with a description of the Odin API in the Scala language, which allows one to apply these rules to arbitrary texts.