UNT College of Engineering - 150 Matching Results

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Abstraction Augmented Markov Models

Description: Article discussing the abstraction augmented Markov models.
Date: December 2010
Creator: Caragea, Cornelia; Silvescu, Adrian; Caragea, Doina & Honavar, Vasant

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-

Anchor Nodes Placement for Effective Passive Localization

Description: This paper discusses anchor nodes placement for effective passive localization. The authors show that, for effective passive localization, the optimal placement of the anchor nodes is at the center of the network in such a way that no three anchor nodes share linearity.
Date: 2011
Creator: Akl, Robert G.; Pasupathy, Karthikeyan & Haidar, Mohamad

Automated measurement of quality of mucosa inspection for colonscopy

Description: This paper from the International Conference on Computational Science conference proceedings presents new methods that derive a new quality metric for automated scoring of quality of mucosa inspection performed by the endoscopist.
Date: May 31, 2010
Creator: Liu, Xuemin; Tavanapong, Wallapak; Wong, Johnny; Oh, JungHwan & de Groen, Piet C.

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-

Characterizing Humour: An Exploration of Features in Humorous Texts

Description: This paper investigates the problem of automatic humor recognition, and provides an in-depth analysis of two of the most frequently observed features of humorous text: human-centeredness and negative polarity. Through experiments performed on two collections of humorous texts, the authors show that these properties of verbal humor are consisted across different data sets.
Date: February 2007
Creator: Mihalcea, Rada, 1974- & Pulman, Stephen