B
Barbara Rosario
Researcher at Intel
Publications - 37
Citations - 3200
Barbara Rosario is an academic researcher from Intel. The author has contributed to research in topics: Noun & Mobile computing. The author has an hindex of 16, co-authored 37 publications receiving 3134 citations. Previous affiliations of Barbara Rosario include Lyons & University of California, Berkeley.
Papers
More filters
Journal ArticleDOI
A Bayesian computer vision system for modeling human interactions
TL;DR: A real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task and demonstrates the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training.
Proceedings ArticleDOI
Classifying Semantic Relations in Bioscience Texts
Barbara Rosario,Marti A. Hearst +1 more
TL;DR: This work examines the problem of distinguishing among seven relation types that can occur between the entities "treatment" and "disease" in bioscience text, and finds that the latter help achieve high classification accuracy.
Book ChapterDOI
A Bayesian Computer Vision System for Modeling Human Interaction
TL;DR: A real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task and the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training is demonstrated.
Proceedings Article
Classifying the Semantic Relations in Noun Compounds via a Domain-Specific Lexical Hierarchy
Barbara Rosario,Marti A. Hearst +1 more
TL;DR: It is found that a very simple approach using a machine learning algorithm and a domain-specific lexical hierarchy successfully generalizes from training instances, performing better on previously unseen words than a baseline consisting of training on the words themselves.
Proceedings ArticleDOI
The Descent of Hierarchy, and Selection in Relational Semantics
TL;DR: This paper explores the possibility of using an existing lexical hierarchy for the purpose of placing words from a noun compound into categories, and then using this category membership to determine the relation that holds between the nouns.