no code implementations • 8 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.
no code implementations • LREC 2016 • Dane Bell, Daniel Fried, Luwen Huangfu, Mihai Surdeanu, Stephen Kobourov
The strategy uses a game-like quiz with data and questions acquired semi-automatically from Twitter.
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.
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 • 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.
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.
no code implementations • NAACL 2019 • George C. G. Barbosa, Zechy Wong, Gus Hahn-Powell, Dane Bell, Rebecca Sharp, Marco A. Valenzuela-Esc{\'a}rcega, Mihai Surdeanu
Many of the most pressing current research problems (e. g., public health, food security, or climate change) require multi-disciplinary collaborations.
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.
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.