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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-
Item Type: Paper

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.
Item Type: Paper

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-
Item Type: Paper

Text Mining for Automatic Image Tagging

Description: This paper introduces several extractive approaches for automatic image tagging, relying exclusively on information mined from texts. Through evaluations on two datasets, the authors show that their methods exceed competitive baselines by a large margin, and compare favorably with the state-of-the-art that uses both textual and image features.
Date: August 2010
Creator: Leong, Chee Wee; Mihalcea, Rada, 1974- & Hassan, Samer
Item Type: Paper

Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation

Description: In this paper, the authors discuss research on whether they can use Mechanical Turk (MTurk) to acquire goo annotations with respect to gold-standard data, whether they can filter out low-quality workers (spammers), and whether there is a learning effect associated with repeatedly completing the same kind of task.
Date: June 2010
Creator: Akkaya, Cem; Conrad, Alexander; Wiebe, Janyce M. & Mihalcea, Rada, 1974-
Item Type: Paper

Reducing Our Carbon Footprint through Ridesharing

Description: This report discusses research on reducing our carbon footprint through ridesharing. The focus of this research project was to analyze and collect travel trajectories to calculate carbon footprints under different travel modes and identify ways to reduce it.
Date: 2012
Creator: Garrett, Kim; Bell, Jesse; Huang, Yan & Powell, James
Item Type: Report

Shared Ride: Transportation, Carbon Footprint and Ridesharing

Description: This poster discusses research on transportation, carbon footprinting and ridesharing. The focus of this research project was to analyze and collect travel trajectories to calculate carbon footprints under different travel modes and identify ways to reduce it.
Date: 2012
Creator: Garrett, Kim; Bell, Jesse; Huang, Yan & Powell, James
Item Type: Poster

Applications of Logic Flowcharting With a Focus in Autonomous Robotic Operations

Description: This report discusses research on applications of logic flowcharting with a focus in autonomous robotic operations. This research project is part of Research Experiences for Teachers (RET) in Sensor Networks, a National Science Foundation (NSF) funded grant project.
Date: 2012
Creator: Sink, Ashley Elizabeth; Gscheidle, Karl H.; Namuduri, Kamesh; Li, Li & Sterling, Phillip
Item Type: Report

Building a Sense Tagged Corpus with Open Mind Word Expert

Description: This paper discusses building a sense tagged corpus with Open Mind Word Expert, an implemented active learning system for collecting word sense tagging from the general public over the Web.
Date: July 2002
Creator: Chklovski, Timothy A. (Timothy Anatolievich), 1977- & Mihalcea, Rada, 1974-
Item Type: Paper