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

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.
Date: 2007
Creator: Michaelides, Efstathios & Mirshams, Reza
Partner: UNT College of Engineering

Answering complex, list and context questions with LCC's Question-Answering Server

Description: This paper presents the architecture of the Question-Answering server (QAS) developed at the Language Computer Corporation (LCC) and used in the TREC-10 evaluations.
Date: November 2001
Creator: Harabagiu, Sanda M.; Moldovan, Dan I.; Paşca, Marius. 1974-; Surdeanu, Mihai; Mihalcea, Rada, 1974-; Gîrju, Corina R. et al.
Partner: UNT College of Engineering

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-
Partner: UNT College of Engineering

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
Partner: UNT College of Engineering

Co-training and Self-training for Word Sense Disambiguation

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.
Date: May 2004
Creator: Mihalcea, Rada, 1974-
Partner: UNT College of Engineering