Search Results for author: Marco A. Valenzuela-Escárcega

Found 7 papers, 3 papers with code

A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction

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.

Relation Extraction

Neural-Guided Program Synthesis of Information Extraction Rules Using Self-Supervision

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.

Language Modelling Program Synthesis

Lightly-supervised Representation Learning with Global Interpretability

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.

Representation Learning

Text Annotation Graphs: Annotating Complex Natural Language Phenomena

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.

Event Extraction TAG +1

This before That: Causal Precedence in the Biomedical Domain

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.

Sieve-based Coreference Resolution in the Biomedical Domain

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.

coreference-resolution Event Coreference Resolution +1

Description of the Odin Event Extraction Framework and Rule Language

1 code implementation24 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.

Event Extraction

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