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open access

Robocamp: Encouraging Young Women to Embrace STEM

Description: This paper describes the efforts and results of a plan for actively recruiting students to undergraduate computer science and engineering programs at the University of North Texas (UNT). Such recruitment of students is critical to the country's efforts to increase the number of engineering professionals, and is a priority for the Computer Science and Engineering (CSE) Department at UNT.
Date: February 2009
Creator: Akl, Robert G. & Keathly, David
open access

Subjectivity Word Sense Disambiguation

Description: This paper investigates a new task, subjectivity word sense disambiguation (SWSD), which is to automatically determine which word instances in a corpus are being used with subjective senses, and which are being used with objective senses.
Date: August 2009
Creator: Akkaya, Cem; Wiebe, Janyce M. & Mihalcea, Rada, 1974-
open access

Synchronization of multiple coupled rf-SQUID flux qubits

Description: Article demonstrating a practical strategy for synchronizing the properties of compound Josephson junction (CJJ) radio frequency monitored superconducting quantum interference device (rf-SQUID) qubits on a multi-qubit chip.
Date: December 21, 2009
Creator: Harris, R.; Brito, F.; Berkley, A. J.; Johansson, J.; Johnson, M. W.; Lanting, T. et al.
open access

Topic Identification Using Wikipedia Graph Centrality

Description: This paper presents a method for automatic topic identification using a graph-centrality algorithm applied to an encyclopedic graph derived from Wikipedia. When tested on a data set with manually assigned topics, the system is found to significantly improve over a simpler baseline that does not make use of the external encyclopedic knowledge.
Date: May 2009
Creator: Coursey, Kino High & Mihalcea, Rada, 1974-
open access

Using Encyclopedic Knowledge for Automatic Topic Identification

Description: This paper presents a method for automatic topic identification using an encyclopedic graph derived from Wikipedia. The system is found to exceed the performance of previously proposed machine learning algorithms for topic identification, with an annotation consistency comparable to human annotations.
Date: May 2009
Creator: Coursey, Kino High; Mihalcea, Rada, 1974- & Moen, William E.
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