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Found 10 papers, 1 papers with code
This before That: Causal Precedence in the Biomedical Domain
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
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.
SnapToGrid: From Statistical to Interpretable Models for Biomedical Information Extraction
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.
Towards using social media to identify individuals at risk for preventable chronic illness
The strategy uses a game-like quiz with data and questions acquired semi-automatically from Twitter.
Analyzing the Language of Food on Social Media
We investigate the predictive power behind the language of food on social media.
Detecting Diabetes Risk from Social Media Activity
This work explores the detection of individuals{'} risk of type 2 diabetes mellitus (T2DM) directly from their social media (Twitter) activity.
Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-domain Free Text
Many of the most pressing current research problems (e. g., public health, food security, or climate change) require multi-disciplinary collaborations.
Odinson: A Fast Rule-based Information Extraction Framework
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.
PatternRank: Jointly Ranking Patterns and Extractions for Relation Extraction Using Graph-Based Algorithms
In this paper we revisit the direction of using lexico-syntactic patterns for relation extraction instead of today’s ubiquitous neural classifiers.
