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Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation

Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation

Date: June 2010
Creator: Akkaya, Cem; Conrad, Alexander; Wiebe, Janyce M. & Mihalcea, Rada, 1974-
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.
Contributing Partner: UNT College of Engineering
An Algorithm for Open Text Semantic Parsing

An Algorithm for Open Text Semantic Parsing

Date: August 2004
Creator: Shi, Lei & Mihalcea, Rada, 1974-
Description: Abstract: This paper describes an algorithm for open text shallow semantic parsing. The algorithm relies on a frame dataset (FrameNet) and a semantic network (WordNet), to identify semantic relations between words in open text, as well as shallow semantic features associated with concepts in the text. Parsing semantic structures allows semantic units and constituents to be accessed and processed in a more meaningful way than syntactic parsing, moving the automation of understanding natural language text to a higher level.
Contributing Partner: UNT College of Engineering
Anchor Nodes Placement for Effective Passive Localization

Anchor Nodes Placement for Effective Passive Localization

Date: 2011
Creator: Akl, Robert G.; Pasupathy, Karthikeyan & Haidar, Mohamad
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.
Contributing Partner: UNT College of Engineering
Attracting and Retaining Women in Computer Science and Engineering: Evaluating the Results

Attracting and Retaining Women in Computer Science and Engineering: Evaluating the Results

Date: June 2007
Creator: Keathly, David & Akl, Robert G.
Description: This paper discusses efforts to attract and retain students in computer science and engineering fields.
Contributing Partner: UNT College of Engineering
AC 2007-1844: An Innovative Mechanical and Energy Engineering Curriculum

AC 2007-1844: An Innovative Mechanical and Energy Engineering Curriculum

Date: 2007
Creator: Michaelides, Efstathios & Mirshams, Reza
Description: This paper discusses the addition of a new Department of Mechanical and Energy Engineering at the University of North Texas (UNT). Those involved see the curriculum for this new program as a new model of engineering education that parallels the innovations of UNTs current Learning to Learn (L2L) project-oriented concept course with the addition of innovative approaches for mechanical engineering and emphasis on energy engineering education.
Contributing Partner: UNT College of Engineering
Transformational Paradigm for Engineering and Engineering Technology Education

Transformational Paradigm for Engineering and Engineering Technology Education

Date: November 2008
Creator: Barbieri, Enrique & Fitzgibbon, William
Description: This paper discusses a transformational paradigm for engineering and engineering technology education at the baccalaureate level.
Contributing Partner: UNT College of Engineering
Tantra: A fast PRNG algorithm and its implementation

Tantra: A fast PRNG algorithm and its implementation

Date: June 2009
Creator: Gomathisankaran, Mahadevan & Lee, Ruby Bei-Loh
Description: This paper discusses Tantra. Tantra is a novel Pseudorandom number generator (PRNG) design that provides a long sequence high quality pseudorandom numbers at very high rate both in software and hardware implementations.
Contributing Partner: UNT College of Engineering
The Decomposition of Human-Written Book Summaries

The Decomposition of Human-Written Book Summaries

Date: March 2009
Creator: Ceylan, Hakan & Mihalcea, Rada, 1974-
Description: In this paper, the authors evaluate the extent to which human-written book summaries can be obtained through cut-and-paste operations from the original book. The authors analyze the effect of the parameters involved in the decomposition algorithm, and highlight the distinctions in coverage obtained for different summary types.
Contributing Partner: UNT College of Engineering
Building a Sense Tagged Corpus with Open Mind Word Expert

Building a Sense Tagged Corpus with Open Mind Word Expert

Date: July 2002
Creator: Chklovski, Timothy A. (Timothy Anatolievich), 1977- & Mihalcea, Rada, 1974-
Description: This paper discusses building a sense tagged corpus with Open Mind Word Expert. Abstract: Open Mind Word Expert is an implemented active learning system for collecting word sense tagging from the general public over the Web. It is available at http://teach-computers.org. The authors expect the system to yield a large volume of high-quality training data at a much lower cost than the traditional method of hiring lexicographers. The authors thus propose a Senseval-3 lexical sample activity where the training data is collected via Open Mind Word Expert. If successful, the collection process can be extended to create the definitive corpus of word sense information.
Contributing Partner: UNT College of Engineering
Co-training and Self-training for Word Sense Disambiguation

Co-training and Self-training for Word Sense Disambiguation

Date: May 2004
Creator: Mihalcea, Rada, 1974-
Description: This paper investigates the application of co-training and self-training to word sense disambiguation. Optimal and empirical parameter selection methods for co-training and self-training are investigated, with various degrees of error reduction. A new method that combines co-training with majority voting is introduced, with the effect of smoothing the bootstrapping learning curves, and improving the average performance.
Contributing Partner: UNT College of Engineering
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