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  Partner: UNT College of Engineering
 Resource Type: Paper
 Collection: UNT Scholarly Works
Thread Specific Features Are Helpful For Identifying Subjectivity Orientation of Online Forum Threads

Thread Specific Features Are Helpful For Identifying Subjectivity Orientation of Online Forum Threads

Date: October 2012
Creator: Biyani, Prakhar; Bhatia, Sumit; Caragea, Cornelia & Mitra, Prasenjit
Description: Paper discussing thread specific features for identifying subjectivity orientation of online forum threads.
Contributing Partner: UNT College of Engineering
Throughput Optimization in Multi-Cell CDMA Networks

Throughput Optimization in Multi-Cell CDMA Networks

Date: March 2005
Creator: Akl, Robert G.; Naraghi-Pour, Mort & Hegde, Manju V.
Description: In this paper, the authors investigate the performance of a multi-cell CDMA network by determining the maximum throughput that the network can archive for a given grade-of-service requirement, quality-of-service requirement, network topology and call arrival rate profile.
Contributing Partner: UNT College of Engineering
Throughput Validation of an Advanced Channel Assignment Algorithm in IEEE 802.11 WLAN

Throughput Validation of an Advanced Channel Assignment Algorithm in IEEE 802.11 WLAN

Date: February 2009
Creator: Haidar, Mohamad; Al-Rizzo, Hussain Mudhaffar Younis, 1957-; Chan, Yupo; Akl, Robert G. & Bouharras, Mohamad
Description: In this article, an enhanced channel-assignment algorithm at the Access Points (APs) of a Wireless Local Area Network (WLAN) is validated.
Contributing Partner: UNT College of Engineering
Topic Identification Using Wikipedia Graph Centrality

Topic Identification Using Wikipedia Graph Centrality

Date: May 2009
Creator: Coursey, Kino High & Mihalcea, Rada, 1974-
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.
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
Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity

Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity

Date: September 2007
Creator: Sinha, Ravi & Mihalcea, Rada, 1974-
Description: This paper describes an unsupervised graph-based method for word sense disambiguation, and presents comparative evaluations using several measures of word semantic similarity and several algorithms for graph centrality. The results indicate that the right combination of similarity metrics and graph centrality algorithms can lead to a performance competing with the state-of-the-art in unsupervised word sense disambiguation, as measured on standard data sets.
Contributing Partner: UNT College of Engineering
Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling

Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling

Date: October 2005
Creator: Mihalcea, Rada, 1974-
Description: This paper introduces a graph-based algorithm for sequence data labeling, using random walks on graphs encoding label dependencies. The algorithm is illustrated and tested in the context of an unsupervised word sense disambiguation problem, and shown to significantly outperform the accuracy achieved through individual label assignment, as measured on standard sense-annotated data sets.
Contributing Partner: UNT College of Engineering
UNT: SubFinder: Combining Knowledge Sources for Automatic Lexical Substitution

UNT: SubFinder: Combining Knowledge Sources for Automatic Lexical Substitution

Date: June 2007
Creator: Hassan, Samer; Csomai, Andras; Banea, Carmen; Sinha, Ravi & Mihalcea, Rada, 1974-
Description: This paper describes the University of North Texas SubFinder system. The system is able to provide the most likely set of substitutes for a word in a given context, by combining several techniques and knowledge sources. SubFinder has successfully participated in the best and out of ten (oot) tracks in the SEMEVAL lexical substitution task, consistently ranking in the first or second place.
Contributing Partner: UNT College of Engineering
UNT-Yahoo: SuperSenseLearner: Combining SenseLearner with SuperSense and other Coarse Semantic Features

UNT-Yahoo: SuperSenseLearner: Combining SenseLearner with SuperSense and other Coarse Semantic Features

Date: June 2007
Creator: Mihalcea, Rada, 1974-; Csomai, Andras & Ciaramita, Massimiliano
Description: This paper discusses combining SenseLearner with SuperSence and other coarse semantic features.
Contributing Partner: UNT College of Engineering
Using Encyclopedic Knowledge for Automatic Topic Identification

Using Encyclopedic Knowledge for Automatic Topic Identification

Date: May 2009
Creator: Coursey, Kino High; Mihalcea, Rada, 1974- & Moen, William E.
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.
Contributing Partner: UNT College of Engineering