Search Results for author: Dane Bell

Found 10 papers, 1 papers with code

PatternRank: Jointly Ranking Patterns and Extractions for Relation Extraction Using Graph-Based Algorithms

no code implementations PANDL (COLING) 2022 Robert Vacareanu, Dane Bell, Mihai Surdeanu

In this paper we revisit the direction of using lexico-syntactic patterns for relation extraction instead of today’s ubiquitous neural classifiers.

Relation 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.

Detecting Diabetes Risk from Social Media Activity

no code implementations WS 2018 Dane Bell, Egoitz Laparra, Aditya Kousik, Terron Ishihara, Mihai Surdeanu, Stephen Kobourov

This work explores the detection of individuals{'} risk of type 2 diabetes mellitus (T2DM) directly from their social media (Twitter) activity.

Domain Adaptation

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.

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 Coreference Resolution +2

Analyzing the Language of Food on Social Media

no code implementations8 Sep 2014 Daniel Fried, Mihai Surdeanu, Stephen Kobourov, Melanie Hingle, Dane Bell

We investigate the predictive power behind the language of food on social media.

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