Search Results for author: Gus Hahn-Powell

Found 17 papers, 4 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

Country-level Arabic Dialect Identification using RNNs with and without Linguistic Features

no code implementations EACL (WANLP) 2021 Elsayed Issa, Mohammed AlShakhori1, Reda Al-Bahrani, Gus Hahn-Powell

This work investigates the value of augmenting recurrent neural networks with feature engineering for the Second Nuanced Arabic Dialect Identification (NADI) Subtask 1. 2: Country-level DA identification.

Attribute Dialect Identification +1

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

Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder

no code implementations ACL 2020 Zheng Tang, Gus Hahn-Powell, Mihai Surdeanu

Our approach uses an encoder-decoder architecture, which jointly trains a classifier for event extraction, and a rule decoder that generates syntactico-semantic rules that explain the decisions of the event classifier.

Decoder Event Extraction

Odinson: A Fast Rule-based Information Extraction Framework

no code implementations LREC 2020 Marco A. Valenzuela-Esc{\'a}rcega, Gus Hahn-Powell, Dane Bell

We present Odinson, a rule-based information extraction framework, which couples a simple yet powerful pattern language that can operate over multiple representations of text, with a runtime system that operates in near real time.

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

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

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.

Odin's Runes: A Rule Language for Information Extraction

no code implementations LREC 2016 Marco A. Valenzuela-Esc{\'a}rcega, Gus Hahn-Powell, Mihai Surdeanu

Odin is an information extraction framework that applies cascades of finite state automata over both surface text and syntactic dependency graphs.

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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